This paper introduces a new technique called species conservation for evolving parallel subpopulations. The technique is based on the concept of dividing the population into several species according to their similarity. Each of these species is built around a dominating individual called the species seed. Species seeds found in the current generation are saved (conserved) by moving them into the next generation. Our technique has proved to be very effective in finding multiple solutions of multimodal optimization problems. We demonstrate this by applying it to a set of test problems, including some problems known to be deceptive to genetic algorithms.@en

A study on the effect of distance measure in genetic algorithm (GM) with species conservation was performed. A technique based on dividing the population into several species according to the similarity for evolving parallel subpopulations for multimodal function optimization was introduced. It was shown that the results found were significantly different from the defined measurement and each solution was a representative of a class of functions.@en

The examination of the distribution of the species seeds in the species conservation genetic algorithms (SCGA) is discussed. A technique based on dividing the population into several species for evolving parallel subpopulations within a genetic algorithm (GA) for multimodal function optimization is introduced. No significant changes in the overall efficiency of the SCGA is observed.@en