Genetic Algorithm with Characteristic Amplification through Multiple Geographically Isolated Populations and Varied Fitness Landscapes

This paper proposes a new approach, wherein multiple populations are evolved on different landscapes. The prob- lem statement is broken down, to describe discrete charac- teristics. Each landscape, described by its fitness landscape is used to optimize or amplify a certain characteristic or set of characteristics. Individuals from each of these pop- ulations are kept geographically isolated from each other. Each population is evolved individually. After a predeter- mined number of evolutions, the system of populations is analysed against a normalized fitness function. Depending on this score and a predefined merging scheme, the popula- tions are merged, one at a time, while continuing evolution. Merging continues until only one final population remains. This population is then evolved, following which the result- ing population will contain the optimal solution. The fi- nal resulting population will contain individuals which have been optimized against all characteristics as desired by the problem statement. Each individual population is optimized for a local maxima. Thus when populations are merged, the effect is to produce a new population which is closer to the global maxima.