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available, e.g. when evolving an abstract picture or colors (Cheng and
Kosorukoff, 2004). In the latter case, human and computational innovation can complement each other, producing cooperative results and improving general user experience by ensuring that spontaneous creativity of users will not be lost.
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is the agent of genetic change. The innovator mutates and recombines the genetic material, to produce the variations on which the selector operates. In most organic and computer-based systems (top and bottom), innovation is automatic, operating without human intervention. In HBGA, the innovators are
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Recent research suggests that human-based innovation operators are advantageous not only where it is hard to design an efficient computational mutation and/or crossover (e.g. when evolving solutions in natural language), but also in the case where good computational innovation operators are readily
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that allows humans to contribute solution suggestions to the evolutionary process. For this purpose, a HBGA has human interfaces for initialization, mutation, and recombinant crossover. As well, it may have interfaces for selective evaluation. In short, a HBGA outsources the operations of a typical
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The HBGA methodology was derived in 1999-2000 from analysis of the Free
Knowledge Exchange project that was launched in the summer of 1998, in Russia (Kosorukoff, 1999). Human innovation and evaluation were used in support of collaborative problem solving. Users were also free to choose the next
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idea while solving a set of problems concurrently. This allows to achieve synergy because solutions can be generalized and reused among several problems. This also facilitates identification of new problems of interest and fair-share resource allocation among problems of different
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is the agent that decides fitness in the system. It determines which variations will reproduce and contribute to the next generation. In natural populations, and in genetic algorithms, these decisions are automatic; whereas in typical HBGA systems, they are made by people.
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The choice of genetic representation, a common problem of genetic algorithms, is greatly simplified in HBGA, since the algorithm need not be aware of the structure of each solution. In particular, HBGA allows natural language to be a valid
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One obvious pattern in the table is the division between organic (top) and computer systems (bottom). Another is the vertical symmetry between autonomous systems (top and bottom) and human-interactive systems (middle).
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HBGA is roughly similar to genetic engineering. In both systems, the innovators and selectors are people. The main difference lies in the genetic material they work with: electronic data vs. polynucleotide sequences.
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Initialization is treated as an operator, rather than a phase of the algorithm. This allows a HBGA to start with an empty population. Initialization, mutation, and crossover operators form the group of innovation
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Cheng, Chihyung
Derrick and Alex Kosorukoff (2004). Interactive one-max problem allows to compare the performance of interactive and human-based genetic algorithms. In
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HBGA is a method of collaboration and knowledge exchange. It merges competence of its human users creating a kind of symbiotic human-machine intelligence (see also
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Among evolutionary genetic systems, HBGA is the computer-based analogue of genetic engineering (Allan, 2005). This table compares systems on lines of human agency:
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All four genetic operators (initialization, mutation, crossover, and selection) can be delegated to humans using appropriate interfaces (Kosorukoff, 2001).
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Human innovation is facilitated by sampling solutions from population, associating and presenting them in different combinations to a user (see
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Choice of genetic operator may be delegated to humans as well, so they are not forced to perform a particular operation at any given moment.
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Education and
Academic benefits from Real Time Simulation with Synthetic Curriculum Modeling using Dynamic Point Cloud environments.
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Allan, Michael (2005). Simple recombinant design. SourceForge.net, project textbender, release 2005.0, file _/description.html.
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Furthermore, human-based genetic algorithms prove to be a successful measure to counteract fatigue effects introduced by
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Kruse, Jan and Connor, Andy (2015). Multi-agent evolutionary systems for the generation of complex virtual worlds.
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genetic operation to perform. Currently, several other projects implement the same model, the most popular being
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Kruse, J.; Connor, A. (2015). "Multi-agent evolutionary systems for the generation of complex virtual worlds".
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Kruse, Jan (2015). Interactive evolutionary computation in design applications for virtual worlds.
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HBGA facilitates consensus and decision making by integrating individual preferences of its users.
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Journal of Information Theories and Applications
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IEEE International
Conference on Systems, Man, and Cybernetics
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ADAN: Adaptive
Newspapers based on Evolutionary Programming
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Kosorukoff, Alex (2001). Human-based genetic algorithm. In
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Kosorukoff, Alex (2000). Human-based genetic algorithm.
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