Parallel Evolutionary Computation Framework for Single- and Multiobjective Optimization

Evolutionary computation is an area of computer science utilizing the mechanisms of biological evolution in computer problem solving. It is concerned with theoretical studies, design and application of stochastic optimization procedures, known as Evolutionary Algorithms (EAs). EAs have proven effective and robust in solving demanding optimization problems that are often difficult if not intractable to traditional numerical methods. They are nowadays widely applied in science, engineering, management, and other domains. However, a drawback of EAs is their computational complexity which originates from iterative population-based search of the solution space. On the other hand, processing a population of candidate solutions makes EAs amenable to parallel implementation that may result in significant calculation speedup.

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