Problem solving: Search, exploration and co-evolution

Genetic Algorithms have become useful problem solving tools and they are frequently applied in the area of optimization. The nature of an optimization problem is that both the search space and the objectives are well-defined in advance. However, back to the nature where the metaphor comes from, an individual species does not evolve in isolation, a species co-evolves with the other species. In this paper, the nature of problem solving is examined and two evolutionary algorithms are proposed to overcome the limitation of a Simple Genetic Algorithm (SGA). The new algorithms are Exploratory Genetic Algorithm (ExpGA) and Go-evolutionary Genetic Algorithm (CoGA) which are capable to handle ill-defined problems. It is further suggested that the SGA is a special case of the CoGA.