Human-based genetic algorithm

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In evolutionary computation, a human-based genetic algorithm (HBGA) is a genetic algorithm that allows humans to contribute their innovative solutions to the evolutionary process. For this purpose HBGA uses human-based innovation interfaces for initialization, mutation, and crossover operators. Often HBGA uses human evaluation as well (see Interactive genetic algorithm). The first online HBGA implementation 3form uses both human innovation and evaluation, to support innovation in a collaborative problem-solving process. In this implementation, human users are also free to choose the next genetic operation to perform. HBGA is a part of a more general class of human-based evolutionary computation methods.

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 available, e.g. when evolving an abstract picture or colors (Cheng, 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.

Contents 1 Structural differences from traditional GA 2 Functional features 3 Areas of application 4 See also 5 References 6 External links

[edit] Structural differences from traditional GA

• Initialization is treated as an operator, rather than a phase of GA. This allows to start HBGA with empty population. Initialization, mutation, and crossover operators form a group of innovation operators.

• All four genetic operators (initialization, mutation, crossover, and selection) can be delegated to humans using appropriate interfaces.

• Choice of genetic operator may be delegated to human as well, so human is not forced to perform a particular operation at any given moment

• Storing and sampling population remains algorithmic function

• HBGA usually uses multiple agents to perform genetic operations, being a typical example of a multi-agent system

[edit] Functional features

• The choice of genetic representation, a common problem of GA, in HBGA is greatly simplified, since algorithms don't have to be aware of the structure of each solution. In particular, HBGA allows a natural language to be a valid representation.
• 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 distributed artificial intelligence).
• Human innovation is facilitated by sampling solutions from population, associating and presenting them in different combinations to a user (see creativity techniques)
• HBGA facilitates consensus and decision making by integrating individual preferences of its users.
• HBGA makes use of a cumulative learning 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 importance.

[edit] Areas of application

• Collaborative problem solving using natural language as a representation

• Evolutionary knowledge management, integration of knowledge from different sources

• Social organization, collective decision-making, and e-governance

• Traditional areas of application of interactive genetic algorithms: computer art, user-centered design, etc.

[edit] See also

Human-based evolutionary computation, Interactive evolutionary computation, Evolutionary computation, Memetics, Interactive genetic algorithm, Social computing, Human-computer interaction

[edit] References

• Kosorukoff, Alex (1999), Free Knowledge Exchange, Internet archive
• Kosorukoff, Alex (2000), Human-based Genetic Algorithm. online
• Kosorukoff, Alex (2001), Human-based Genetic Algorithm. IEEE Transactions on Systems, Man, and Cybernetics, SMC-2001, 3464-3469.Fulltext
• Kosorukoff, A. & Goldberg, D. E. (2002) Genetic algorithm as a form of organization, Proceedings of Genetic and Evolutionary Computation Conference, GECCO-2002, pp 965-972
• David Goldberg (2002) The design of innovation: Lessons from and for Competent Genetic Algorithms, Springer
• Defaweux, A., Grosche, T., Karapatsiou, M., Moraglio, A., Shenfield, A. (2003). Automated Concept Evolution. Technical Report, EvoNet Summer School, University of Parma, Italy.
• Cheng, Chihyung Derrick and Kosorukoff, Alex (2004), Interactive one-max problem allows to compare the performance of interactive and human-based genetic algorithms. Genetic and Evolutionary Computational Conference, GECCO-2004.
• Terence Claus Fogarty (2005) Automatic Concept Evolution (ACE) In Proceedings of GECCO-2005, June 25-29, Washington DC
• Michelle Okaley Hammond, Terence Claus Fogarty (2005) Co-operative OuLiPian Generative Literature using Human Based Evolutionary Computing. In Proceedings of GECCO-2005, June 25-29, Washington DC
• Pradeep Kumar, Tapas Mahapatra, Shalini Khandelwal (2005) Transformed Organization for Effective E-Governance through Genetic Algorihtm. In M. P. Gupta (Ed) Promise of E-Governance: Operational Challenges. IIT Press, Delhi. p 188-194

[edit] External links

• [1] - Free Knowledge Exchange, a project using HBGA for collaborative solving of problems expressed in natural language.
• [2] - Interactive one-max problem allows to compare the performance of interactive and human-based genetic algorithms.