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). Applications 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. Collaborative problem solving using natural language as a representation. Education and Academic benefits from Real Time Simulation with Synthetic Curriculum Modeling using Dynamic Point Cloud environments. 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 genetic operation to perform. Currently, several other projects implement the same model, the most popular being Yahoo! Answers, launched in December 2005. 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 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. Furthermore, human-based genetic algorithms prove to be a successful measure to counteract fatigue effects introduced by interactive genetic algorithms. See also Human-based computation Human-based evolutionary computation Human–computer interaction Interactive genetic algorithm Memetics Social computing References Kosorukoff, Alex (1999). Free knowledge exchange. internet archive Kosorukoff, Alex (2000). Human-based genetic algorithm. online Kosorukoff, Alex (2001). Human-based genetic algorithm. In IEEE International Conference on Systems, Man, and Cybernetics, SMC-2001, 3464-3469. full text Cheng, Chihyung Derrick and Alex Kosorukoff (2004). Interactive one-max problem allows to compare the