Author(s)

R.G. Cembrowicz

Abstract

Evolution Strategies (ES) and Genetic Algorithms (GA) are becoming algorithmic tools in engineering. Traditional algorithms of Operations Research used for optimization of water resources and environmental systems comprised Linear and Dynamic Programming, Nonlinear Convex Programming, Branch & Bound Methods, Integer Programming, Gradient Methods a.o.. Often, the complexity of real-world problems limited the success of rigorous algorithms. The systems analyst was forced to resort to search methods. The latter would contain stochastic elements. Recently, this philosophy has been extended by introducing principles of biological evolution also based upon stochastic elements, called here 'gene mutation', 'crossover', or 'recombination'. Thes

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