1479:
individual learning (exploitation) in the MA search, for a given fixed limited computational budget. Clearly, a more intense individual learning provides greater chance of convergence to the local optima but limits the amount of evolution that may be expended without incurring excessive computational resources. Therefore, care should be taken when setting these two parameters to balance the computational budget available in achieving maximum search performance. When only a portion of the population individuals undergo learning, the issue of which subset of individuals to improve need to be considered to maximize the utility of MA search. Last but not least, it has to be decided whether the respective individual should be changed by the learning success (Lamarckian learning) or not (Baldwinian learning). Thus, the following five design questions must be answered, the first of which is addressed by all of the above 2nd generation representatives during an MA run, while the extended form of meta-Lamarckian learning of expands this to the first four design decisions.
389:(MC). With MC, the traits of universal Darwinism are more appropriately captured. Viewed in this perspective, MA is a more constrained notion of MC. More specifically, MA covers one area of MC, in particular dealing with areas of evolutionary algorithms that marry other deterministic refinement techniques for solving optimization problems. MC extends the notion of memes to cover conceptual entities of knowledge-enhanced procedures or representations.
1461:
generating local improvements through a reward mechanism, deciding on which meme to be selected to proceed for future local refinements. Memes with a higher reward have a greater chance of continuing to be used. For a review on second generation MA; i.e., MA considering multiple individual learning methods within an evolutionary system, the reader is referred to.
1520:
On the issue of selecting appropriate individuals among the EA population that should undergo individual learning, fitness-based and distribution-based strategies were studied for adapting the probability of applying individual learning on the population of chromosomes in continuous parametric search
1511:
One of the first issues pertinent to memetic algorithm design is to consider how often the individual learning should be applied; i.e., individual learning frequency. In one case, the effect of individual learning frequency on MA search performance was considered where various configurations of the
1502:
In combinatorial optimization, on the other hand, individual learning methods commonly exist in the form of heuristics (which can be deterministic or stochastic) that are tailored to a specific problem of interest. Typical heuristic procedures and schemes include the k-gene exchange, edge exchange,
1469:
Co-evolution and self-generating MAs may be regarded as 3rd generation MA where all three principles satisfying the definitions of a basic evolving system have been considered. In contrast to 2nd generation MA which assumes that the memes to be used are known a priori, 3rd generation MA utilizes a
401:
state that all optimization strategies are equally effective with respect to the set of all optimization problems. Conversely, this means that one can expect the following: The more efficiently an algorithm solves a problem or class of problems, the less general it is and the more problem-specific
1460:
is then used to perform a local refinement. The memetic material is then transmitted through a simple inheritance mechanism from parent to offspring(s). On the other hand, in hyper-heuristic and meta-Lamarckian MA, the pool of candidate memes considered will compete, based on their past merits in
428:, since all the core principles of inheritance/memetic transmission, variation, and selection are missing. This suggests why the term MA stirred up criticisms and controversies among researchers when first introduced. The following pseudo code would correspond to this general definition of an MA:
1478:
The learning method/meme used has a significant impact on the improvement results, so care must be taken in deciding which meme or memes to use for a particular optimization problem. The frequency and intensity of individual learning directly define the degree of evolution (exploration) against
1723:
IEEE Workshop on
Memetic Algorithms (WOMA 2009). Program Chairs: Jim Smith, University of the West of England, U.K.; Yew-Soon Ong, Nanyang Technological University, Singapore; Gustafson Steven, University of Nottingham; U.K.; Meng Hiot Lim, Nanyang Technological University, Singapore; Natalio
1571:
It is to be decided whether a found improvement is to work only by the better fitness (Baldwinian learning) or whether also the individual is adapted accordingly (lamarckian learning). In the case of an EA, this would mean an adjustment of the genotype. This question has been controversially
417:
Memetic algorithms are a marriage between a population-based global search and the heuristic local search made by each of the individuals. ... The mechanisms to do local search can be to reach a local optimum or to improve (regarding the objective cost function) up to a predetermined
1512:
individual learning frequency at different stages of the MA search were investigated. Conversely, it was shown elsewhere that it may be worthwhile to apply individual learning on every individual if the computational complexity of the individual learning is relatively low.
329:. The term MA is now widely used as a synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. Quite often, MAs are also referred to in the literature as Baldwinian
624:
leaves the chromosome unchanged and uses only the improved fitness. This pseudo code leaves open which steps are based on the fitness of the individuals and which are not. In question are the evolving of the new population and the selection of
361:(GA) coupled with an individual learning procedure capable of performing local refinements. The metaphorical parallels, on the one hand, to Darwinian evolution and, on the other hand, between memes and domain specific (local search)
402:
knowledge it builds on. This insight leads directly to the recommendation to complement generally applicable metaheuristics with application-specific methods or heuristics, which fits well with the concept of MAs.
1451:
and meta-Lamarckian MA are referred to as second generation MA exhibiting the principles of memetic transmission and selection in their design. In Multi-meme MA, the memetic material is encoded as part of the
424:". This original definition of MA although encompasses characteristics of cultural evolution (in the form of local refinement) in the search cycle, it may not qualify as a true evolving system according to
1563:, is the amount of computational budget allocated to an iteration of individual learning; i.e., the maximum computational budget allowable for individual learning to expend on improving a single solution.
1111:
1487:
In the context of continuous optimization, individual learning exists in the form of local heuristics or conventional exact enumerative methods. Examples of individual learning strategies include the
1525:
problems. Bambha et al. introduced a simulated heating technique for systematically integrating parameterized individual learning into evolutionary algorithms to achieve maximum solution quality.
1588:
Memetic algorithms have been successfully applied to a multitude of real-world problems. Although many people employ techniques closely related to memetic algorithms, alternative names such as
1197:
1034:
3052:
Wehrens, R.; Lucasius, C.; Buydens, L.; Kateman, G. (1993). "HIPS, A hybrid self-adapting expert system for nuclear magnetic resonance spectrum interpretation using genetic algorithms".
781:
365:
are captured within memetic algorithms thus rendering a methodology that balances well between generality and problem specificity. This two-stage nature makes them a special case of
1572:
discussed for EAs in the literature already in the 1990s, stating that the specific use case plays a major role. The background of the debate is that genome adaptation may promote
653:
529:
493:
1385:
1432:
1304:
955:
273:
921:
1759:
1470:
rule-based local search to supplement candidate solutions within the evolutionary system, thus capturing regularly repeated features or patterns in the problem space.
1400:
Instead of all offspring, only a randomly selected or fitness-dependent fraction may undergo local improvement. The latter requires the evaluation of the offspring in
1149:
986:
1561:
1241:
601:
568:
1337:
1270:
880:
851:
822:
725:
658:
Since most MA implementations are based on EAs, the pseudo code of a corresponding representative of the first generation is also given here, following
Krasnogor:
696:
2709:
Orvosh, David; Davis, Lawrence (1993), Forrest, Stephanie (ed.), "Shall We Repair? Genetic
Algorithms, Combinatorial Optimization, and Feasibility Constraints",
1824:
Poonam Garg (April 2009). "A Comparison between
Memetic algorithm and Genetic algorithm for the cryptanalysis of Simplified Data Encryption Standard algorithm".
1931:
2627:
Bambha N. K. and
Bhattacharyya S. S. and Teich J. and Zitzler E. (2004). "Systematic integration of parameterized local search into evolutionary algorithms".
3017:
Augugliaro, A.; Dusonchet, L.; Riva-Sanseverino, E. (1998). "Service restoration in compensated distribution networks using a hybrid genetic algorithm".
1577:
3383:
Areibi, S.; Yang, Z. (2004). "Effective memetic algorithms for VLSI design automation = genetic algorithms + local search + multi-level clustering".
398:
2436:
Advances in Nature-Inspired
Computation: The PPSN VII Workshops. PEDAL (Parallel Emergent and Distributed Architectures Lab). University of Reading
2853:
Aguilar, J.; Colmenares, A. (1998). "Resolution of pattern recognition problems using a hybrid genetic/random neural network learning algorithm".
1968:
1804:
232:
3647:
3447:
3205:
2915:
2762:
2718:
2611:
1905:
1798:
266:
2184:
Burke E. K.; Gendreau M.; Hyde M.; Kendall G.; Ochoa G.; Ouml; zcan E.; Qu R. (2013). "Hyper-heuristics: A Survey of the State of the Art".
101:
2888:
Ridao, M.; Riquelme, J.; Camacho, E.; Toro, M. (1998). "An evolutionary and local search algorithm for planning two manipulators motion".
1746:
372:
In the context of complex optimization, many different instantiations of memetic algorithms have been reported across a wide range of
2539:
2084:
1691:
179:
3431:
1889:
3676:
259:
126:
34:
2114:, Caltech Concurrent Computation Program, Technical Report 826, Pasadena, CA: California Institute of Technology, pp. 19β20
620:
in this context means to update the chromosome according to the improved solution found by the individual learning step, while
61:
1632:
159:
1756:
1576:. This risk can be effectively mitigated by other measures to better balance breadth and depth searches, such as the use of
3109:. Proceedings of the 5th International Conference of the Decision Sciences Institute. Athens, Greece. pp. 1708β1710.
2982:
Harris, S.; Ifeachor, E. (1998). "Automatic design of frequency sampling filters by hybrid genetic algorithm techniques".
212:
189:
169:
131:
2741:
Whitley, Darrell; Gordon, V. Scott; Mathias, Keith (1994), Davidor, Yuval; Schwefel, Hans-Paul; MΓ€nner, Reinhard (eds.),
1772:
1790:
1612:
227:
174:
3299:"Fast Rescheduling of Multiple Workflows to Constrained Heterogeneous Resources Using Multi-Criteria Memetic Computing"
2434:
Krasnogor N. & Gustafson S. (2002). "Toward truly "memetic" memetic algorithms: discussion and proof of concepts".
1499:, line search, and other local heuristics. Note that most of the common individual learning methods are deterministic.
376:, in general, converging to high-quality solutions more efficiently than their conventional evolutionary counterparts.
1608:
315:
237:
106:
3630:
G. Karkavitsas & G. Tsihrintzis (2011). "Automatic Music Genre
Classification Using Hybrid Genetic Algorithms".
3297:
Jakob, Wilfried; Strack, Sylvia; Quinte, Alexander; Bengel, GΓΌnther; Stucky, Karl-Uwe; SΓΌΓ, Wolfgang (2013-04-22).
2931:
Haas, O.; Burnham, K.; Mills, J. (1998). "Optimization of beam orientation in radiotherapy using planar geometry".
1966:
Chen, X. S.; Ong, Y. S.; Lim, M. H. (2010). "Research
Frontier: Memetic Computation - Past, Present & Future".
1522:
217:
164:
116:
3498:
Zexuan Zhu, Y. S. Ong and M. Dash (2007). "Wrapper-Filter
Feature Selection Algorithm Using A Memetic Framework".
1045:
2555:
Ku, K. W. C.; Mak, M. W.; Siu., W. C (2000). "A study of the
Lamarckian evolution of recurrent neural networks".
1884:
Moscato, P.; Mathieson, L. (2019). "Memetic Algorithms for Business Analytics and Data Science: A Brief Survey".
1710:, feature/gene selection, parameter determination for hardware fault injection, and multi-class, multi-objective
1671:
1647:
1496:
1870:, Caltech Concurrent Computation Program, Technical Report 826, Pasadena, CA: California Institute of Technology
730:
326:
81:
3551:
1683:
1624:
3463:
Zexuan Zhu, Y. S. Ong and M. Dash (2007). "Markov Blanket-Embedded Genetic Algorithm for Gene Selection".
3347:
2893:
2597:
2461:
2193:
1787:, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, Vol. 37, No. 1, February 2007.
1687:
698:; // Initialization of the generation counter Randomly generate an initial population
330:
303:
71:
51:
42:
2834:. IEEE International Joint Conference on Neural Networks. Vol. 2. New York, NY. pp. 1131β1136.
1154:
991:
1768:
1659:
1573:
1492:
319:
222:
184:
2327:
1929:
Chen, X. S.; Ong, Y. S.; Lim, M. H.; Tan, K. C. (2011). "A Multi-Facet Survey on Memetic Computation".
357:
in his technical report in 1989 where he viewed MA as being close to a form of population-based hybrid
3552:"Artificial Intelligence for Fault Injection Parameter Selection | Marina KrΔek | Hardwear.io Webinar"
3472:
3061:
3026:
2991:
2940:
1843:
1707:
1679:
366:
307:
3352:
2898:
2466:
2198:
1483:
Selection of an individual learning method or meme to be used for a particular problem or individual
2602:
2111:
On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms
1867:
On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms
1651:
1628:
425:
354:
295:
203:
136:
91:
2664:"Adding Learning to the Cellular Development of Neural Networks: Evolution and the Baldwin Effect"
2165:
628:
504:
468:
318:
technique to improve the quality of solutions generated by the EA and to reduce the likelihood of
3653:
3612:
3533:
3408:
3365:
3338:
Ozcan, E.; Basaran, C. (2009). "A Case Study of Memetic Algorithms for Constraint Optimization".
3279:
3238:
3168:
3145:
3110:
3087:
2964:
2870:
2812:
2724:
2691:
2644:
2510:
2416:
2366:
2305:
2253:
2211:
2057:
2034:
1995:
1948:
1911:
1833:
1639:
1349:
86:
66:
3107:
Memetic algorithms to minimize tardiness on a single machine with sequence-dependent setup times
445:
Generate an initial population, evaluate the individuals and assign a quality value to them;
2173:. 4th Asia-Pacific Conference on Simulated Evolution and Learning. SEAL 2002. pp. 667β671.
891:
3643:
3604:
3596:
3525:
3443:
3400:
3320:
3201:
3128:
Costa, Daniel (1995). "An Evolutionary Tabu Search Algorithm And The NHL Scheduling Problem".
2956:
2911:
2804:
2758:
2714:
2683:
2607:
2535:
2502:
2408:
2358:
2297:
2090:
2080:
1901:
1794:
1711:
1616:
381:
358:
299:
150:
96:
76:
2126:
1778:
1757:
IEEE Computational Intelligence Society Emergent Technologies Task Force on Memetic Computing
1743:
3635:
3588:
3515:
3507:
3480:
3435:
3392:
3357:
3310:
3269:
3230:
3193:
3137:
3077:
3069:
3034:
2999:
2948:
2903:
2862:
2835:
2796:
2750:
2675:
2636:
2572:
2564:
2494:
2400:
2350:
2342:
2289:
2245:
2203:
2026:
1985:
1977:
1940:
1893:
1703:
1604:
1536:
1211:
576:
543:
291:
247:
141:
1403:
1310:
1275:
926:
3426:
Merz, P.; Zell, A. (2002). "Clustering Gene Expression Profiles with Memetic Algorithms".
1763:
1750:
1655:
1600:
1596:
1448:
1246:
856:
827:
798:
701:
342:
56:
675:
3634:. Smart Innovation, Systems and Technologies. Vol. 11. Springer. pp. 323β335.
3476:
3065:
3030:
2995:
2944:
2892:. Lecture Notes in Computer Science. Vol. 1416. Springer-Verlag. pp. 105β114.
1847:
1781:
by Thomson Scientific's Essential Science Indicators as an Emerging Front Research Area.
1129:
966:
3221:
Ozcan, E.; Onbasioglu, E. (2007). "Memetic Algorithms for Parallel Code Optimization".
3192:. Lecture Notes in Computer Science. Vol. 3867. Springer-Verlag. pp. 85β104.
2663:
1663:
1620:
621:
121:
3038:
3670:
3073:
2968:
2952:
2784:
2742:
2529:
2482:
2277:
1915:
1667:
1488:
194:
3657:
3537:
3369:
3283:
3242:
3149:
3114:
3091:
2874:
2728:
2695:
2420:
2257:
2061:
1999:
1952:
3616:
3412:
3141:
2648:
2514:
2215:
2143:
2109:
2038:
1865:
1643:
379:
In general, using the ideas of memetics within a computational framework is called
2816:
2370:
2309:
1737:
17:
3639:
3484:
2230:
1897:
3197:
2749:, vol. 866, Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 5β15,
3576:
2832:
Learning of neural networks with parallel hybrid GA using a royal road function
1784:
3520:
3511:
3361:
3234:
2907:
2839:
2800:
2754:
2679:
2404:
2346:
2293:
2014:
1944:
1457:
617:
3600:
3439:
3396:
3324:
2808:
2687:
2640:
2506:
2498:
2385:
2301:
2249:
2052:
Wolpert, D. H.; Macready, W. G. (1995). "No Free Lunch Theorems for Search".
1690:, maintenance scheduling (for example, of an electric distribution network),
460:
all individuals in the population and assign a quality value to them.
3167:. Proceedings of young operational research conference 1998. Guildford, UK.
2785:"A general cost-benefit-based adaptation framework for multimeme algorithms"
2278:"A general cost-benefit-based adaptation framework for multimeme algorithms"
2094:
1981:
1879:
1877:
362:
325:
Memetic algorithms represent one of the recent growing areas of research in
311:
3608:
3529:
3404:
3258:"A memetic algorithm to schedule planned maintenance for the national grid"
2412:
2362:
1698:
to constrained heterogeneous resources, multidimensional knapsack problem,
3592:
3274:
3257:
2960:
2207:
1727:
1695:
1453:
3500:
IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics
2594:
Evolutionary Algorithms with Local Search for Combinatorial Optimization
2568:
2455:
2393:
IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics
2335:
IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics
2030:
1990:
1826:
International Journal of Network Security & Its Applications (IJNSA)
1744:
Special Issue on 'Emerging Trends in Soft Computing - Memetic Algorithm'
3340:
Soft Computing: A Fusion of Foundations, Methodologies and Applications
3082:
2866:
242:
3003:
2481:
Hart, William E.; Krasnogor, Natalio; Smith, Jim E. (September 2004).
2354:
1394:
All or some of the initial individuals may be improved by the meme(s).
3315:
3298:
2577:
333:(EAs), Lamarckian EAs, cultural algorithms, or genetic local search.
2743:"Lamarckian evolution, the Baldwin effect and function optimization"
2328:"Classification of Adaptive Memetic Algorithms: A Comparative Study"
1516:
Selection of the individuals to which individual learning is applied
496:, that should undergo the individual improvement procedure.
3173:
2131:(PhD). Bristol, UK: University of the West of England. p. 23.
1838:
1734:
2008 IEEE World Congress on Computational Intelligence (WCCI 2008)
1595:
Researchers have used memetic algorithms to tackle many classical
3581:
IEEE/ACM Transactions on Computational Biology and Bioinformatics
3188:
Ozcan, E. (2007). "Memes, Self-generation and Nurse Rostering".
1699:
346:
1456:. Subsequently, the decoded meme of each respective individual/
341:
Inspired by both Darwinian principles of natural evolution and
2711:
Conf. Proc. of the 5th Int. Conf. on Genetic Algorithms (ICGA)
1675:
2386:"Coevolving Memetic Algorithms: A Review and Progress Report"
1793:, Series: Studies in Fuzziness and Soft Computing, Vol. 166,
1397:
The parents may be locally improved instead of the offspring.
1390:
There are some alternatives for this MA scheme. For example:
2483:"Editorial Introduction Special Issue on Memetic Algorithms"
2128:
Studies on the Theory and Design Space of Memetic Algorithms
1807:, Evolutionary Computation Fall 2004, Vol. 12, No. 3: v-vi.
1733:
456:
a new population using stochastic search operators.
3575:
Zhu, Zexuan; Ong, Yew-Soon; Zurada, Jacek M (April 2010).
2326:
Ong Y. S. and Lim M. H. and Zhu N. and Wong K. W. (2006).
1638:
More recent applications include (but are not limited to)
3577:"Identification of Full and Partial Class Relevant Genes"
1753:, Soft Computing Journal, Completed & In Press, 2008.
1674:, automatic timetabling (notably, the timetable for the
422:
I am not constraining an MA to a genetic representation.
306:(EA). It may provide a sufficiently good solution to an
3632:
Intelligent Interactive Multimedia Systems and Services
2144:"Coevolution of genes and memes in memetic algorithms"
3430:. Lecture Notes in Computer Science. Vol. 2439.
1539:
1529:
Specification of the intensity of individual learning
1406:
1352:
1313:
1278:
1249:
1214:
1157:
1132:
1048:
994:
969:
929:
894:
859:
830:
801:
733:
704:
678:
631:
579:
546:
507:
471:
2890:
Tasks and Methods in Applied Artificial Intelligence
2713:, San Mateo, CA, USA: Morgan Kaufmann, p. 650,
2662:
Gruau, FrΓ©dΓ©ric; Whitley, Darrell (September 1993).
1769:
IEEE Congress on Evolutionary Computation (CEC 2007)
3130:
INFOR: Information Systems and Operational Research
2596:(Thesis). San Diego, CA: University of California.
1555:
1507:Determination of the individual learning frequency
1426:
1379:
1331:
1298:
1264:
1235:
1191:
1143:
1105:
1028:
980:
949:
915:
874:
845:
816:
775:
719:
690:
647:
595:
562:
523:
487:
2159:
2157:
399:no-free-lunch theorems of optimization and search
2460:(PhD). San Diego, CA: University of California.
2231:"Meta-Lamarckian learning in memetic algorithms"
3428:Parallel Problem Solving from Nature β PPSN VII
3190:Practice and Theory of Automated Timetabling VI
2747:Parallel Problem Solving from Nature β PPSN III
608:with Lamarckian or Baldwinian learning.
415:Pablo Moscato characterized an MA as follows: "
2457:Adaptive Global Optimization with Local Search
2449:
2447:
2445:
1859:
1857:
1491:, Simplex method, Newton/Quasi-Newton method,
3223:International Journal of Parallel Programming
2629:IEEE Transactions on Evolutionary Computation
2557:IEEE Transactions on Evolutionary Computation
2321:
2319:
2238:IEEE Transactions on Evolutionary Computation
2019:IEEE Transactions on Evolutionary Computation
1932:IEEE Transactions on Evolutionary Computation
267:
8:
3105:França, P.; Mendes, A.; Moscato, P. (1999).
2013:Wolpert, D.H.; Macready, W.G. (April 1997).
1106:{\displaystyle f(p')\ \ \forall p'\in M'(t)}
2186:Journal of the Operational Research Society
1567:Choice of Lamarckian or Baldwinian learning
2167:Choice function and random hyperheuristics
2164:Kendall G. and Soubeiga E. and Cowling P.
1886:Business and Consumer Analytics: New Ideas
1339:; // Increment the generation counter
274:
260:
29:
3519:
3351:
3314:
3273:
3172:
3081:
2897:
2601:
2576:
2465:
2197:
2015:"No free lunch theorems for optimization"
1989:
1837:
1724:Krasnogor, University of Nottingham, U.K.
1544:
1538:
1521:problems with Land extending the work to
1405:
1351:
1312:
1277:
1248:
1213:
1156:
1131:
1047:
993:
968:
928:
893:
858:
829:
800:
776:{\displaystyle f(p)\ \ \forall p\in P(t)}
732:
727:; Compute the fitness
703:
677:
636:
630:
584:
578:
551:
545:
512:
506:
476:
470:
1969:IEEE Computational Intelligence Magazine
1666:, electric service restoration, medical
3165:Nurse rostering with genetic algorithms
1816:
1730:, first issue appeared in January 2009.
1718:Recent activities in memetic algorithms
202:
149:
41:
27:Algorithm for searching a problem space
2984:IEEE Transactions on Signal Processing
787:Stopping conditions are not satisfied
449:Stopping conditions are not satisfied
2271:
2269:
2267:
1791:Recent Advances in Memetic Algorithms
1773:Special Session on Memetic Algorithms
1738:Special Session on Memetic Algorithms
298:, is an extension of the traditional
7:
3262:Journal of Experimental Algorithmics
2229:Y. S. Ong & A. J. Keane (2004).
1503:first-improvement, and many others.
669:Memetic Algorithm Based on an EA
102:Evolutionary multimodal optimization
2830:Ichimura, T.; Kuriyama, Y. (1998).
2079:. New York: Van Nostrand Reinhold.
1805:Special Issue on Memetic Algorithms
1785:Special Issue on Memetic Algorithms
1243:by selecting some individuals from
1192:{\displaystyle \forall p'\in M'(t)}
1029:{\displaystyle \forall p'\in M'(t)}
2783:Jakob, Wilfried (September 2010).
2454:Hart, William E. (December 1994).
2276:Jakob, Wilfried (September 2010).
1158:
1072:
995:
752:
633:
538:individual learning using meme(s)
509:
473:
25:
2855:Pattern Analysis and Applications
2054:Technical Report SFI-TR-95-02-010
888:Recombine and mutate individuals
540:with frequency or probability of
373:
1599:problems. To cite some of them:
127:Promoter based genetic algorithm
3019:Electric Power Systems Research
2933:Physics in Medicine and Biology
1533:Individual learning intensity,
62:Cellular evolutionary algorithm
3142:10.1080/03155986.1995.11732279
2077:Handbook of Genetic Algorithms
1633:generalized assignment problem
1421:
1415:
1374:
1362:
1293:
1287:
1259:
1253:
1230:
1218:
1186:
1180:
1100:
1094:
1063:
1052:
1023:
1017:
944:
938:
910:
904:
869:
863:
840:
834:
811:
805:
770:
764:
743:
737:
714:
708:
1:
3256:Burke, E.; Smith, A. (1999).
3039:10.1016/S0378-7796(98)00025-X
2531:Evolution and Optimum Seeking
988:by local search or heuristic
213:Cartesian genetic programming
132:Spiral optimization algorithm
3640:10.1007/978-3-642-22158-3_32
3485:10.1016/j.patcog.2007.02.007
3074:10.1016/0003-2670(93)80444-P
2528:Schwefel, Hans-Paul (1995).
1898:10.1007/978-3-030-06222-4_13
1684:nurse rostering optimisation
1613:quadratic assignment problem
648:{\displaystyle \Omega _{il}}
524:{\displaystyle \Omega _{il}}
488:{\displaystyle \Omega _{il}}
228:Multi expression programming
3198:10.1007/978-3-540-77345-0_6
2142:Krasnogor, Natalio (1999).
2125:Krasnogor, Natalio (2002).
1625:max independent set problem
1609:travelling salesman problem
1380:{\displaystyle p\in P(t-1)}
465:the subset of individuals,
107:Particle swarm optimization
3693:
2953:10.1088/0031-9155/43/8/013
1648:artificial neural networks
1523:combinatorial optimization
218:Linear genetic programming
165:Clonal selection algorithm
117:Natural evolution strategy
3512:10.1109/TSMCB.2006.883267
3362:10.1007/s00500-008-0354-4
3235:10.1007/s10766-006-0026-x
2908:10.1007/3-540-64574-8_396
2840:10.1109/IJCNN.1998.685931
2801:10.1007/s12293-010-0040-9
2755:10.1007/3-540-58484-6_245
2680:10.1162/evco.1993.1.3.213
2405:10.1109/TSMCB.2006.883273
2347:10.1109/TSMCB.2005.856143
2294:10.1007/s12293-010-0040-9
2148:Graduate Student Workshop
1945:10.1109/tevc.2011.2132725
1728:Memetic Computing Journal
1672:single machine scheduling
1605:multidimensional knapsack
1590:hybrid genetic algorithms
1497:conjugate gradient method
1151:according to improvement
916:{\displaystyle p\in M(t)}
3440:10.1007/3-540-45712-7_78
3397:10.1162/1063656041774947
3385:Evolutionary Computation
2668:Evolutionary Computation
2641:10.1109/TEVC.2004.823471
2499:10.1162/1063656041775009
2487:Evolutionary Computation
2250:10.1109/TEVC.2003.819944
2075:Davis, Lawrence (1991).
1708:gene expression profiles
327:evolutionary computation
82:Evolutionary computation
3677:Evolutionary algorithms
2592:Land, M. W. S. (1998).
2108:Moscato, Pablo (1989),
1982:10.1109/mci.2010.936309
1864:Moscato, Pablo (1989),
353:(MA) was introduced by
331:evolutionary algorithms
3054:Analytica Chimica Acta
2056:. Santa Fe Institute.
1621:minimal graph coloring
1578:structured populations
1557:
1556:{\displaystyle t_{il}}
1493:interior point methods
1428:
1381:
1333:
1300:
1266:
1237:
1236:{\displaystyle P(t+1)}
1193:
1145:
1107:
1030:
982:
951:
917:
876:
847:
818:
777:
721:
692:
649:
597:
596:{\displaystyle t_{il}}
564:
563:{\displaystyle f_{il}}
525:
489:
406:The development of MAs
393:Theoretical Background
304:evolutionary algorithm
72:Differential evolution
52:Artificial development
43:Evolutionary algorithm
3593:10.1109/TCBB.2008.105
3275:10.1145/347792.347801
3163:Aickelin, U. (1998).
1574:premature convergence
1558:
1429:
1427:{\displaystyle M'(t)}
1382:
1334:
1332:{\displaystyle t=t+1}
1301:
1299:{\displaystyle M'(t)}
1267:
1238:
1194:
1146:
1108:
1031:
983:
952:
950:{\displaystyle M'(t)}
918:
877:
848:
819:
778:
722:
693:
650:
598:
573:with an intensity of
565:
526:
490:
441:Memetic Algorithm
420:" And he emphasizes "
320:premature convergence
310:. It uses a suitable
302:(GA) or more general
223:Grammatical evolution
185:Genetic fuzzy systems
3434:. pp. 811β820.
2384:Smith J. E. (2007).
2208:10.1057/jors.2013.71
1892:. pp. 545β608.
1688:processor allocation
1592:are also employed.
1537:
1404:
1350:
1311:
1276:
1265:{\displaystyle P(t)}
1247:
1212:
1155:
1130:
1118:Lamarckian learning
1046:
1042:Compute the fitness
992:
967:
927:
892:
875:{\displaystyle M(t)}
857:
846:{\displaystyle P(t)}
828:
817:{\displaystyle f(p)}
799:
731:
720:{\displaystyle P(t)}
702:
676:
629:
577:
544:
505:
469:
367:dual-phase evolution
308:optimization problem
3477:2007PatRe..40.3236Z
3465:Pattern Recognition
3066:1993AcAC..277..313W
3031:1998EPSR...46...59A
2996:1998ITSP...46.3304H
2945:1998PMB....43.2179H
2569:10.1109/4235.843493
2534:. New York: Wiley.
2031:10.1109/4235.585893
1848:2010arXiv1004.0574G
1779:'Memetic Computing'
1680:manpower scheduling
1652:pattern recognition
1629:bin packing problem
824:choose a subset of
691:{\displaystyle t=0}
622:Baldwinian learning
618:Lamarckian learning
501:each individual in
426:universal Darwinism
387:memetic computation
374:application domains
296:operations research
233:Genetic Improvement
204:Genetic programming
137:Self-modifying code
92:Gaussian adaptation
3521:10338.dmlcz/141593
2867:10.1007/BF01238026
1762:2011-09-27 at the
1749:2011-09-27 at the
1640:business analytics
1601:graph partitioning
1553:
1424:
1377:
1329:
1296:
1262:
1233:
1189:
1144:{\displaystyle p'}
1141:
1103:
1026:
981:{\displaystyle p'}
978:
947:
923:and store them in
913:
872:
843:
814:
773:
717:
688:
645:
593:
560:
521:
485:
87:Evolution strategy
67:Cultural algorithm
18:Memetic algorithms
3649:978-3-642-22157-6
3471:(11): 3236β3248.
3449:978-3-540-44139-7
3207:978-3-540-77344-3
3004:10.1109/78.735305
2990:(12): 3304β3314.
2917:978-3-540-64574-0
2789:Memetic Computing
2764:978-3-540-58484-1
2720:978-1-55860-299-1
2613:978-0-599-12661-9
2282:Memetic Computing
2192:(12): 1695β1724.
1907:978-3-030-06221-7
1799:978-3-540-22904-9
1712:feature selection
1617:set cover problem
1474:Some design notes
1071:
1068:
751:
748:
382:memetic computing
359:genetic algorithm
351:memetic algorithm
300:genetic algorithm
288:memetic algorithm
284:
283:
151:Genetic algorithm
112:Memetic algorithm
97:Grammar induction
77:Effective fitness
16:(Redirected from
3684:
3662:
3661:
3627:
3621:
3620:
3572:
3566:
3565:
3563:
3562:
3548:
3542:
3541:
3523:
3495:
3489:
3488:
3460:
3454:
3453:
3423:
3417:
3416:
3380:
3374:
3373:
3355:
3346:(8β9): 871β882.
3335:
3329:
3328:
3318:
3316:10.3390/a6020245
3294:
3288:
3287:
3277:
3253:
3247:
3246:
3218:
3212:
3211:
3185:
3179:
3178:
3176:
3160:
3154:
3153:
3125:
3119:
3118:
3102:
3096:
3095:
3085:
3049:
3043:
3042:
3014:
3008:
3007:
2979:
2973:
2972:
2939:(8): 2179β2193.
2928:
2922:
2921:
2901:
2885:
2879:
2878:
2850:
2844:
2843:
2827:
2821:
2820:
2780:
2774:
2773:
2772:
2771:
2738:
2732:
2731:
2706:
2700:
2699:
2659:
2653:
2652:
2624:
2618:
2617:
2605:
2589:
2583:
2582:
2580:
2552:
2546:
2545:
2525:
2519:
2518:
2478:
2472:
2471:
2469:
2451:
2440:
2439:
2431:
2425:
2424:
2390:
2381:
2375:
2374:
2332:
2323:
2314:
2313:
2273:
2262:
2261:
2235:
2226:
2220:
2219:
2201:
2181:
2175:
2174:
2172:
2161:
2152:
2151:
2139:
2133:
2132:
2122:
2116:
2115:
2105:
2099:
2098:
2072:
2066:
2065:
2049:
2043:
2042:
2010:
2004:
2003:
1993:
1963:
1957:
1956:
1926:
1920:
1919:
1881:
1872:
1871:
1861:
1852:
1851:
1841:
1821:
1562:
1560:
1559:
1554:
1552:
1551:
1433:
1431:
1430:
1425:
1414:
1386:
1384:
1383:
1378:
1346:best individual
1338:
1336:
1335:
1330:
1307:
1305:
1303:
1302:
1297:
1286:
1271:
1269:
1268:
1263:
1242:
1240:
1239:
1234:
1200:
1198:
1196:
1195:
1190:
1179:
1168:
1150:
1148:
1147:
1142:
1140:
1114:
1112:
1110:
1109:
1104:
1093:
1082:
1069:
1066:
1062:
1036:
1035:
1033:
1032:
1027:
1016:
1005:
987:
985:
984:
979:
977:
958:
956:
954:
953:
948:
937:
922:
920:
919:
914:
883:
881:
879:
878:
873:
853:and store it in
852:
850:
849:
844:
823:
821:
820:
815:
782:
780:
779:
774:
749:
746:
726:
724:
723:
718:
697:
695:
694:
689:
654:
652:
651:
646:
644:
643:
604:
602:
600:
599:
594:
592:
591:
570:
569:
567:
566:
561:
559:
558:
534:
530:
528:
527:
522:
520:
519:
495:
494:
492:
491:
486:
484:
483:
292:computer science
276:
269:
262:
248:Parity benchmark
142:Polymorphic code
30:
21:
3692:
3691:
3687:
3686:
3685:
3683:
3682:
3681:
3667:
3666:
3665:
3650:
3629:
3628:
3624:
3574:
3573:
3569:
3560:
3558:
3550:
3549:
3545:
3497:
3496:
3492:
3462:
3461:
3457:
3450:
3425:
3424:
3420:
3382:
3381:
3377:
3353:10.1.1.368.7327
3337:
3336:
3332:
3296:
3295:
3291:
3255:
3254:
3250:
3220:
3219:
3215:
3208:
3187:
3186:
3182:
3162:
3161:
3157:
3127:
3126:
3122:
3104:
3103:
3099:
3051:
3050:
3046:
3016:
3015:
3011:
2981:
2980:
2976:
2930:
2929:
2925:
2918:
2899:10.1.1.324.2668
2887:
2886:
2882:
2852:
2851:
2847:
2829:
2828:
2824:
2782:
2781:
2777:
2769:
2767:
2765:
2740:
2739:
2735:
2721:
2708:
2707:
2703:
2661:
2660:
2656:
2626:
2625:
2621:
2614:
2591:
2590:
2586:
2554:
2553:
2549:
2542:
2527:
2526:
2522:
2480:
2479:
2475:
2467:10.1.1.473.1370
2453:
2452:
2443:
2433:
2432:
2428:
2388:
2383:
2382:
2378:
2330:
2325:
2324:
2317:
2275:
2274:
2265:
2233:
2228:
2227:
2223:
2199:10.1.1.384.9743
2183:
2182:
2178:
2170:
2163:
2162:
2155:
2141:
2140:
2136:
2124:
2123:
2119:
2107:
2106:
2102:
2087:
2074:
2073:
2069:
2051:
2050:
2046:
2012:
2011:
2007:
1965:
1964:
1960:
1928:
1927:
1923:
1908:
1883:
1882:
1875:
1863:
1862:
1855:
1823:
1822:
1818:
1814:
1764:Wayback Machine
1751:Wayback Machine
1720:
1656:motion planning
1586:
1569:
1540:
1535:
1534:
1531:
1518:
1509:
1485:
1476:
1467:
1449:hyper-heuristic
1445:
1407:
1402:
1401:
1388:
1348:
1347:
1309:
1308:
1279:
1274:
1273:
1245:
1244:
1210:
1209:
1207:
1205:New generation:
1172:
1161:
1153:
1152:
1133:
1128:
1127:
1122:
1086:
1075:
1055:
1044:
1043:
1038:
1009:
998:
990:
989:
970:
965:
964:
959:
930:
925:
924:
890:
889:
884:
855:
854:
826:
825:
797:
796:
795:Accordingly to
791:
729:
728:
700:
699:
674:
673:
671:Initialization:
632:
627:
626:
615:
580:
575:
574:
572:
547:
542:
541:
539:
508:
503:
502:
497:
472:
467:
466:
461:
413:
408:
395:
339:
280:
57:Artificial life
28:
23:
22:
15:
12:
11:
5:
3690:
3688:
3680:
3679:
3669:
3668:
3664:
3663:
3648:
3622:
3587:(2): 263β277.
3567:
3543:
3490:
3455:
3448:
3418:
3391:(3): 327β353.
3375:
3330:
3309:(2): 245β277.
3289:
3248:
3213:
3206:
3180:
3155:
3136:(3): 161β178.
3120:
3097:
3060:(2): 313β324.
3044:
3009:
2974:
2923:
2916:
2880:
2845:
2822:
2795:(3): 201β218.
2775:
2763:
2733:
2719:
2701:
2674:(3): 213β233.
2654:
2635:(2): 137β155.
2619:
2612:
2603:10.1.1.55.8986
2584:
2547:
2540:
2520:
2473:
2441:
2426:
2376:
2341:(1): 141β152.
2315:
2288:(3): 201β218.
2263:
2221:
2176:
2153:
2134:
2117:
2100:
2085:
2067:
2044:
2005:
1958:
1939:(5): 591β607.
1921:
1906:
1873:
1853:
1815:
1813:
1810:
1809:
1808:
1802:
1788:
1782:
1776:
1766:
1754:
1741:
1731:
1725:
1719:
1716:
1668:expert systems
1664:circuit design
1646:, training of
1585:
1582:
1568:
1565:
1550:
1547:
1543:
1530:
1527:
1517:
1514:
1508:
1505:
1484:
1481:
1475:
1472:
1466:
1465:3rd generation
1463:
1444:
1443:2nd generation
1441:
1440:
1439:
1423:
1420:
1417:
1413:
1410:
1398:
1395:
1376:
1373:
1370:
1367:
1364:
1361:
1358:
1355:
1328:
1325:
1322:
1319:
1316:
1295:
1292:
1289:
1285:
1282:
1261:
1258:
1255:
1252:
1232:
1229:
1226:
1223:
1220:
1217:
1188:
1185:
1182:
1178:
1175:
1171:
1167:
1164:
1160:
1139:
1136:
1126:chromosome of
1102:
1099:
1096:
1092:
1089:
1085:
1081:
1078:
1074:
1065:
1061:
1058:
1054:
1051:
1025:
1022:
1019:
1015:
1012:
1008:
1004:
1001:
997:
976:
973:
946:
943:
940:
936:
933:
912:
909:
906:
903:
900:
897:
871:
868:
865:
862:
842:
839:
836:
833:
813:
810:
807:
804:
772:
769:
766:
763:
760:
757:
754:
745:
742:
739:
736:
716:
713:
710:
707:
687:
684:
681:
665:
664:
663:
642:
639:
635:
590:
587:
583:
557:
554:
550:
518:
515:
511:
482:
479:
475:
437:
436:
435:
433:
412:
411:1st generation
409:
407:
404:
394:
391:
338:
335:
282:
281:
279:
278:
271:
264:
256:
253:
252:
251:
250:
245:
240:
235:
230:
225:
220:
215:
207:
206:
200:
199:
198:
197:
192:
187:
182:
180:Genetic memory
177:
172:
167:
162:
154:
153:
147:
146:
145:
144:
139:
134:
129:
124:
122:Neuroevolution
119:
114:
109:
104:
99:
94:
89:
84:
79:
74:
69:
64:
59:
54:
46:
45:
39:
38:
26:
24:
14:
13:
10:
9:
6:
4:
3:
2:
3689:
3678:
3675:
3674:
3672:
3659:
3655:
3651:
3645:
3641:
3637:
3633:
3626:
3623:
3618:
3614:
3610:
3606:
3602:
3598:
3594:
3590:
3586:
3582:
3578:
3571:
3568:
3557:
3553:
3547:
3544:
3539:
3535:
3531:
3527:
3522:
3517:
3513:
3509:
3505:
3501:
3494:
3491:
3486:
3482:
3478:
3474:
3470:
3466:
3459:
3456:
3451:
3445:
3441:
3437:
3433:
3429:
3422:
3419:
3414:
3410:
3406:
3402:
3398:
3394:
3390:
3386:
3379:
3376:
3371:
3367:
3363:
3359:
3354:
3349:
3345:
3341:
3334:
3331:
3326:
3322:
3317:
3312:
3308:
3304:
3300:
3293:
3290:
3285:
3281:
3276:
3271:
3267:
3263:
3259:
3252:
3249:
3244:
3240:
3236:
3232:
3228:
3224:
3217:
3214:
3209:
3203:
3199:
3195:
3191:
3184:
3181:
3175:
3170:
3166:
3159:
3156:
3151:
3147:
3143:
3139:
3135:
3131:
3124:
3121:
3116:
3112:
3108:
3101:
3098:
3093:
3089:
3084:
3079:
3075:
3071:
3067:
3063:
3059:
3055:
3048:
3045:
3040:
3036:
3032:
3028:
3024:
3020:
3013:
3010:
3005:
3001:
2997:
2993:
2989:
2985:
2978:
2975:
2970:
2966:
2962:
2958:
2954:
2950:
2946:
2942:
2938:
2934:
2927:
2924:
2919:
2913:
2909:
2905:
2900:
2895:
2891:
2884:
2881:
2876:
2872:
2868:
2864:
2860:
2856:
2849:
2846:
2841:
2837:
2833:
2826:
2823:
2818:
2814:
2810:
2806:
2802:
2798:
2794:
2790:
2786:
2779:
2776:
2766:
2760:
2756:
2752:
2748:
2744:
2737:
2734:
2730:
2726:
2722:
2716:
2712:
2705:
2702:
2697:
2693:
2689:
2685:
2681:
2677:
2673:
2669:
2665:
2658:
2655:
2650:
2646:
2642:
2638:
2634:
2630:
2623:
2620:
2615:
2609:
2604:
2599:
2595:
2588:
2585:
2579:
2574:
2570:
2566:
2562:
2558:
2551:
2548:
2543:
2541:0-471-57148-2
2537:
2533:
2532:
2524:
2521:
2516:
2512:
2508:
2504:
2500:
2496:
2492:
2488:
2484:
2477:
2474:
2468:
2463:
2459:
2458:
2450:
2448:
2446:
2442:
2437:
2430:
2427:
2422:
2418:
2414:
2410:
2406:
2402:
2398:
2394:
2387:
2380:
2377:
2372:
2368:
2364:
2360:
2356:
2352:
2348:
2344:
2340:
2336:
2329:
2322:
2320:
2316:
2311:
2307:
2303:
2299:
2295:
2291:
2287:
2283:
2279:
2272:
2270:
2268:
2264:
2259:
2255:
2251:
2247:
2244:(2): 99β110.
2243:
2239:
2232:
2225:
2222:
2217:
2213:
2209:
2205:
2200:
2195:
2191:
2187:
2180:
2177:
2169:
2168:
2160:
2158:
2154:
2149:
2145:
2138:
2135:
2130:
2129:
2121:
2118:
2113:
2112:
2104:
2101:
2096:
2092:
2088:
2086:0-442-00173-8
2082:
2078:
2071:
2068:
2063:
2059:
2055:
2048:
2045:
2040:
2036:
2032:
2028:
2024:
2020:
2016:
2009:
2006:
2001:
1997:
1992:
1987:
1983:
1979:
1975:
1971:
1970:
1962:
1959:
1954:
1950:
1946:
1942:
1938:
1934:
1933:
1925:
1922:
1917:
1913:
1909:
1903:
1899:
1895:
1891:
1887:
1880:
1878:
1874:
1869:
1868:
1860:
1858:
1854:
1849:
1845:
1840:
1835:
1831:
1827:
1820:
1817:
1811:
1806:
1803:
1800:
1796:
1792:
1789:
1786:
1783:
1780:
1777:
1774:
1771:, Singapore,
1770:
1767:
1765:
1761:
1758:
1755:
1752:
1748:
1745:
1742:
1739:
1736:, Hong Kong,
1735:
1732:
1729:
1726:
1722:
1721:
1717:
1715:
1713:
1709:
1705:
1701:
1697:
1693:
1689:
1685:
1681:
1677:
1673:
1669:
1665:
1662:orientation,
1661:
1657:
1653:
1649:
1645:
1641:
1636:
1634:
1630:
1626:
1622:
1618:
1614:
1610:
1606:
1602:
1598:
1593:
1591:
1583:
1581:
1579:
1575:
1566:
1564:
1548:
1545:
1541:
1528:
1526:
1524:
1515:
1513:
1506:
1504:
1500:
1498:
1494:
1490:
1489:hill climbing
1482:
1480:
1473:
1471:
1464:
1462:
1459:
1455:
1450:
1442:
1437:
1434:prior to the
1418:
1411:
1408:
1399:
1396:
1393:
1392:
1391:
1371:
1368:
1365:
1359:
1356:
1353:
1345:
1342:
1326:
1323:
1320:
1317:
1314:
1290:
1283:
1280:
1256:
1250:
1227:
1224:
1221:
1215:
1206:
1203:
1183:
1176:
1173:
1169:
1165:
1162:
1137:
1134:
1125:
1121:
1117:
1097:
1090:
1087:
1083:
1079:
1076:
1059:
1056:
1049:
1041:
1020:
1013:
1010:
1006:
1002:
999:
974:
971:
962:
941:
934:
931:
907:
901:
898:
895:
887:
866:
860:
837:
831:
808:
802:
794:
790:
786:
767:
761:
758:
755:
740:
734:
711:
705:
685:
682:
679:
672:
668:
661:
660:
659:
656:
640:
637:
623:
619:
614:
611:
607:
588:
585:
581:
555:
552:
548:
537:
533:
516:
513:
500:
480:
477:
464:
459:
455:
452:
448:
444:
440:
434:
431:
430:
429:
427:
423:
419:
410:
405:
403:
400:
392:
390:
388:
384:
383:
377:
375:
370:
368:
364:
360:
356:
355:Pablo Moscato
352:
348:
344:
336:
334:
332:
328:
323:
321:
317:
313:
309:
305:
301:
297:
293:
289:
277:
272:
270:
265:
263:
258:
257:
255:
254:
249:
246:
244:
241:
239:
236:
234:
231:
229:
226:
224:
221:
219:
216:
214:
211:
210:
209:
208:
205:
201:
196:
195:Fly algorithm
193:
191:
188:
186:
183:
181:
178:
176:
173:
171:
168:
166:
163:
161:
158:
157:
156:
155:
152:
148:
143:
140:
138:
135:
133:
130:
128:
125:
123:
120:
118:
115:
113:
110:
108:
105:
103:
100:
98:
95:
93:
90:
88:
85:
83:
80:
78:
75:
73:
70:
68:
65:
63:
60:
58:
55:
53:
50:
49:
48:
47:
44:
40:
36:
32:
31:
19:
3631:
3625:
3584:
3580:
3570:
3559:. Retrieved
3555:
3546:
3506:(1): 70β76.
3503:
3499:
3493:
3468:
3464:
3458:
3427:
3421:
3388:
3384:
3378:
3343:
3339:
3333:
3306:
3302:
3292:
3265:
3261:
3251:
3229:(1): 33β61.
3226:
3222:
3216:
3189:
3183:
3164:
3158:
3133:
3129:
3123:
3106:
3100:
3057:
3053:
3047:
3025:(1): 59β66.
3022:
3018:
3012:
2987:
2983:
2977:
2936:
2932:
2926:
2889:
2883:
2861:(1): 52β61.
2858:
2854:
2848:
2831:
2825:
2792:
2788:
2778:
2768:, retrieved
2746:
2736:
2710:
2704:
2671:
2667:
2657:
2632:
2628:
2622:
2593:
2587:
2563:(1): 31β42.
2560:
2556:
2550:
2530:
2523:
2490:
2486:
2476:
2456:
2435:
2429:
2396:
2392:
2379:
2338:
2334:
2285:
2281:
2241:
2237:
2224:
2189:
2185:
2179:
2166:
2147:
2137:
2127:
2120:
2110:
2103:
2076:
2070:
2053:
2047:
2025:(1): 67β82.
2022:
2018:
2008:
1991:10356/148175
1976:(2): 24β36.
1973:
1967:
1961:
1936:
1930:
1924:
1885:
1866:
1829:
1825:
1819:
1694:of multiple
1644:data science
1637:
1594:
1589:
1587:
1584:Applications
1570:
1532:
1519:
1510:
1501:
1486:
1477:
1468:
1447:Multi-meme,
1446:
1435:
1389:
1343:
1340:
1204:
1201:
1123:
1119:
1115:
1039:
960:
885:
792:
788:
784:
670:
666:
657:
616:
612:
609:
605:
535:
531:
498:
462:
457:
453:
450:
446:
442:
438:
421:
416:
414:
396:
386:
380:
378:
371:
350:
345:notion of a
340:
337:Introduction
324:
316:local search
287:
285:
111:
3556:hardwear.io
3268:(4): 1β13.
3083:2066/112321
2493:(3): vβvi.
2399:(1): 6β17.
1387:as result;
1040:Evaluation:
662:Pseudo code
443:Initialize:
432:Pseudo code
349:, the term
3561:2021-05-21
3303:Algorithms
2770:2023-02-07
2355:10220/4653
1812:References
1704:clustering
1692:scheduling
1654:, robotic
1458:chromosome
886:Offspring:
793:Selection:
363:heuristics
160:Chromosome
3601:1545-5963
3348:CiteSeerX
3325:1999-4893
3174:1004.2870
2969:250856984
2894:CiteSeerX
2809:1865-9284
2791:. p.207.
2688:1063-6560
2598:CiteSeerX
2578:10397/289
2507:1063-6560
2462:CiteSeerX
2302:1865-9284
2194:CiteSeerX
1916:173187844
1839:1004.0574
1696:workflows
1369:−
1357:∈
1341:end while
1208:Generate
1170:∈
1159:∀
1084:∈
1073:∀
1037:;
1007:∈
996:∀
961:Learning:
899:∈
759:∈
753:∀
667:Procedure
634:Ω
613:end while
510:Ω
474:Ω
439:Procedure
312:heuristic
190:Selection
170:Crossover
3671:Category
3658:15011089
3609:20431146
3538:18382400
3530:17278560
3432:Springer
3405:15355604
3370:17032624
3284:17174080
3243:15182941
3150:15491435
3115:10797987
3092:53954763
2875:15803359
2729:10098180
2696:15048360
2421:13867280
2413:17278554
2363:16468573
2258:11003004
2095:23081440
2062:12890367
2000:17955514
1953:17006589
1890:Springer
1760:Archived
1747:Archived
1702:design,
1454:genotype
1436:Learning
1412:′
1284:′
1177:′
1166:′
1138:′
1091:′
1080:′
1060:′
1014:′
1003:′
975:′
963:Improve
935:′
458:Evaluate
343:Dawkins'
290:(MA) in
175:Mutation
35:a series
33:Part of
3617:2904028
3473:Bibcode
3413:2190268
3062:Bibcode
3027:Bibcode
2992:Bibcode
2961:9725597
2941:Bibcode
2649:8303351
2515:9912363
2216:3053192
2039:5553697
1844:Bibcode
1801:, 2005.
610:end for
606:Proceed
536:Perform
243:Eurisko
3656:
3646:
3615:
3607:
3599:
3536:
3528:
3446:
3411:
3403:
3368:
3350:
3323:
3282:
3241:
3204:
3148:
3113:
3090:
2967:
2959:
2914:
2896:
2873:
2817:167807
2815:
2807:
2761:
2727:
2717:
2694:
2686:
2647:
2610:
2600:
2538:
2513:
2505:
2464:
2419:
2411:
2371:818688
2369:
2361:
2310:167807
2308:
2300:
2256:
2214:
2196:
2150:: 371.
2093:
2083:
2060:
2037:
1998:
1951:
1914:
1904:
1797:
1631:, and
1344:Return
1124:Update
1070:
1067:
750:
747:
463:Select
454:Evolve
418:level.
238:Schema
37:on the
3654:S2CID
3613:S2CID
3534:S2CID
3409:S2CID
3366:S2CID
3280:S2CID
3239:S2CID
3169:arXiv
3146:S2CID
3111:S2CID
3088:S2CID
2965:S2CID
2871:S2CID
2813:S2CID
2725:S2CID
2692:S2CID
2645:S2CID
2511:S2CID
2417:S2CID
2389:(PDF)
2367:S2CID
2331:(PDF)
2306:S2CID
2254:S2CID
2234:(PDF)
2212:S2CID
2171:(PDF)
2058:S2CID
2035:S2CID
1996:S2CID
1949:S2CID
1912:S2CID
1834:arXiv
1832:(1).
1438:step.
785:while
783:;
447:while
3644:ISBN
3605:PMID
3597:ISSN
3526:PMID
3444:ISBN
3401:PMID
3321:ISSN
3202:ISBN
2957:PMID
2912:ISBN
2805:ISSN
2759:ISBN
2715:ISBN
2684:ISSN
2608:ISBN
2536:ISBN
2503:ISSN
2409:PMID
2359:PMID
2298:ISSN
2091:OCLC
2081:ISBN
1902:ISBN
1795:ISBN
1700:VLSI
1660:beam
1642:and
1272:and
1120:then
397:The
347:meme
294:and
3636:doi
3589:doi
3516:hdl
3508:doi
3481:doi
3436:doi
3393:doi
3358:doi
3311:doi
3270:doi
3231:doi
3194:doi
3138:doi
3078:hdl
3070:doi
3058:277
3035:doi
3000:doi
2949:doi
2904:doi
2863:doi
2836:doi
2797:doi
2751:doi
2676:doi
2637:doi
2573:hdl
2565:doi
2495:doi
2401:doi
2351:hdl
2343:doi
2290:doi
2246:doi
2204:doi
2027:doi
1986:hdl
1978:doi
1941:doi
1894:doi
1706:of
1678:),
1676:NHL
499:for
385:or
314:or
3673::
3652:.
3642:.
3611:.
3603:.
3595:.
3583:.
3579:.
3554:.
3532:.
3524:.
3514:.
3504:37
3502:.
3479:.
3469:49
3467:.
3442:.
3407:.
3399:.
3389:12
3387:.
3364:.
3356:.
3344:13
3342:.
3319:.
3305:.
3301:.
3278:.
3264:.
3260:.
3237:.
3227:35
3225:.
3200:.
3144:.
3134:33
3132:.
3086:.
3076:.
3068:.
3056:.
3033:.
3023:46
3021:.
2998:.
2988:46
2986:.
2963:.
2955:.
2947:.
2937:43
2935:.
2910:.
2902:.
2869:.
2857:.
2811:.
2803:.
2787:.
2757:,
2745:,
2723:,
2690:.
2682:.
2670:.
2666:.
2643:.
2631:.
2606:.
2571:.
2559:.
2509:.
2501:.
2491:12
2489:.
2485:.
2444:^
2415:.
2407:.
2397:37
2395:.
2391:.
2365:.
2357:.
2349:.
2339:36
2337:.
2333:.
2318:^
2304:.
2296:.
2284:.
2280:.
2266:^
2252:.
2240:.
2236:.
2210:.
2202:.
2190:64
2188:.
2156:^
2146:.
2089:.
2033:.
2021:.
2017:.
1994:.
1984:.
1972:.
1947:.
1937:15
1935:.
1910:.
1900:.
1888:.
1876:^
1856:^
1842:.
1828:.
1714:.
1686:,
1682:,
1670:,
1658:,
1650:,
1635:.
1627:,
1623:,
1619:,
1615:,
1611:,
1607:,
1603:,
1597:NP
1580:.
1495:,
1202:fi
1116:if
789:do
655:.
571:,
532:do
451:do
369:.
322:.
286:A
3660:.
3638::
3619:.
3591::
3585:7
3564:.
3540:.
3518::
3510::
3487:.
3483::
3475::
3452:.
3438::
3415:.
3395::
3372:.
3360::
3327:.
3313::
3307:6
3286:.
3272::
3266:4
3245:.
3233::
3210:.
3196::
3177:.
3171::
3152:.
3140::
3117:.
3094:.
3080::
3072::
3064::
3041:.
3037::
3029::
3006:.
3002::
2994::
2971:.
2951::
2943::
2920:.
2906::
2877:.
2865::
2859:1
2842:.
2838::
2819:.
2799::
2793:2
2753::
2698:.
2678::
2672:1
2651:.
2639::
2633:8
2616:.
2581:.
2575::
2567::
2561:4
2544:.
2517:.
2497::
2470:.
2438:.
2423:.
2403::
2373:.
2353::
2345::
2312:.
2292::
2286:2
2260:.
2248::
2242:8
2218:.
2206::
2097:.
2064:.
2041:.
2029::
2023:1
2002:.
1988::
1980::
1974:5
1955:.
1943::
1918:.
1896::
1850:.
1846::
1836::
1830:1
1775:.
1740:.
1549:l
1546:i
1542:t
1422:)
1419:t
1416:(
1409:M
1375:)
1372:1
1366:t
1363:(
1360:P
1354:p
1327:1
1324:+
1321:t
1318:=
1315:t
1306:;
1294:)
1291:t
1288:(
1281:M
1260:)
1257:t
1254:(
1251:P
1231:)
1228:1
1225:+
1222:t
1219:(
1216:P
1199:;
1187:)
1184:t
1181:(
1174:M
1163:p
1135:p
1113:;
1101:)
1098:t
1095:(
1088:M
1077:p
1064:)
1057:p
1053:(
1050:f
1024:)
1021:t
1018:(
1011:M
1000:p
972:p
957:;
945:)
942:t
939:(
932:M
911:)
908:t
905:(
902:M
896:p
882:;
870:)
867:t
864:(
861:M
841:)
838:t
835:(
832:P
812:)
809:p
806:(
803:f
771:)
768:t
765:(
762:P
756:p
744:)
741:p
738:(
735:f
715:)
712:t
709:(
706:P
686:0
683:=
680:t
641:l
638:i
603:.
589:l
586:i
582:t
556:l
553:i
549:f
517:l
514:i
481:l
478:i
275:e
268:t
261:v
20:)
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.