EVOLUTION STOCHASTIC OPTIMIZATION ALGORITHMS: GENETIC ALGORITHMS AND EVOLUTION STRATEGIES
The applicability of multiparameter systems is determined by
their optimization for the desired task. New methods of
multiparameter system optimization have been published recently.
They are, for example, neural networks and evolution stochastic
optimization algorithms. Design and analysis of evolution
stochastic optimization algorithms requires unambiguous
formulating and possibility to predict the algorithm behaviour
while designing them. The paper presents design and analysis of
genetic algorithms (evolution strategies), new genetic
algorithms (evolution strategies) with a distributed genotype,
and new distributed genetic algorithms (evolution strategies).
These algorithms can be used (in addition to other fields of
artificial intelligence) for rectangle packing applications.
Keywords: genetic algorithms, evolution strategies, genetic algorithms and evolution strategies with a distributed genotype, distributed genetic algorithms and evolution strategies, rectangle packing applications