Parallel evolutionary algorithms can achieve super-linear performance

Parallel evolutionary algorithms can achieve super-linear performance

One of the main reasons for using parallel evolutionary algorithms (PEAs) is to obtain efficient algorithms with an execution time much lower than that of their sequential counterparts in order, e.g., to tackle more complex problems. This naturally leads to measuring the speedup of the PEA. PEAs have sometimes been reported to provide super-linear performances for different problems, parameteri...

Achieving super-linear performance in parallel multi-objective evolutionary algorithms by means of cooperative coevolution

This article introduces three new multi-objective cooperative coevolutionary variants of three state-ofthe-art multi-objective evolutionary algorithms, namely, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Multi-objective Cellular Genetic Algorithm (MOCell). In such a coevolutionary architecture, the population is split into several s...

Highly Parallel Evolutionary Algorithms

This work describes massively parallel genetic algorithms as a means for solving diicult global optimization, symbolic expression inference and curve tting problems. Results are shown for hard test cases and the implications for computational physics problems are discussed. The approach, eminently parallel, is believed to be competitive with more familiar methodologies used by computational sci...

Parallel Evolutionary Algorithms

Parallel Multi-objective Evolutionary Algorithms

This paper describes a general overview of parallel multi-objective evolutionary algorithms (MOEA) from the design and the implementation point of views. A unified taxonomy using three hierarchical parallel models is proposed. Different parallel architectures are considered. The performance evaluation issue of parallel MOEA is also discussed.