The impact of population sizes and diversity on the adaptability of evolution strategies in dynamic environments

In time-dependent optimization problems, the main task for a problem solver is not to find a good solution, but to track the moving best solution. It is well-known that evolutionary algorithms (EA) can cope with this requirement. A main attribute of many EA is the self-adaptability. The functioning of this feature depends on the setting of several EA parameters. In case of evolution strategies, it is still unknown under which conditions the algorithm is able to converge against the optimum. Our investigations concern different population sizes /spl mu/ and /spl lambda/ as well as the correlation between the best function value and the diversity of the population on some selected test functions.