EvA: a tool for optimization with evolutionary algorithms

We describe the EvA software package which consists of parallel (and sequential) implementations of genetic algorithms (GAs) and evolution strategies (ESs) and a common graphical user interface. We concentrate on the descriptions of the two distributed implementations of GAs and ESs which are of most interest for the future. We present comparisons of different kinds of genetic algorithms and evolution strategies that include implementations of distributed algorithms on the Intel Paragon, a large MIMD computer and massively parallel algorithms on a 16384 processor MasPar MP-1, a large SIMD computer. The results show that parallelization of evolution strategies not only achieves a speedup in execution time of the algorithm, but also a higher probability of convergence and an increase of quality of the achieved solutions. In the benchmark functions we tested, the distributed ESs have a better performance than the distributed GAs.