Functional Features
- 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.
- 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 representation.
- Storing and sampling population usually remains an algorithmic function.
- A HBGA is usually a multi-agent system, delegating genetic operations to multiple agents (humans).
Read more about this topic: Human-based Genetic Algorithm
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