ABSTRACT
This chapter addresses some issues including those relating to parallel implementation of Evolutionary Computing (EC). Genetic algorithms (GA) developed by J. H. Holland and his group of coworkers are a class of algorithms that exploit the idea of biological evolution. Evolutionary programming (EP) derives its operational strength from its core concept of simulating the adaptive behavior noted in the evolutionary process of nature. The emphasis in EP is on simulating the adaptive behavior in the evolutionary scheme instead of simulating the genetics of evolution as practiced in GA. Evolutionary strategy (ES) is a computing paradigm which exploits the ability of a population to evolve its innate evolvability, perhaps even optimize it and in the process solve a problem. Differential evolution (DE) is a variant of evolutionary computing technique operationally different from the other EC techniques discussed so far. GAs or other EC techniques including the technique of DE make use of a population of evolving solutions.
