Novel Evolutionary Methods in Engineering Optimization
Towards Robustness and Efficiency
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In industry there is a high demand for methods that can efficiently solve search problems. Evolutionary Computing (EC) is proven to be well suited to solve search problems, especially optimization tasks. However, despite the advantages and increasing popularity, there are numerous open questions related to the design and parameter tuning of these algorithms. To answer these questions the author of this book introduces neutral evolutionary search achieved by objective function transformation(s) resulting several advantageous features: (a) Simplifies the design of an evolutionary solver by givin...
In industry there is a high demand for methods that can efficiently solve search problems. Evolutionary Computing (EC) is proven to be well suited to solve search problems, especially optimization tasks. However, despite the advantages and increasing popularity, there are numerous open questions related to the design and parameter tuning of these algorithms. To answer these questions the author of this book introduces neutral evolutionary search achieved by objective function transformation(s) resulting several advantageous features: (a) Simplifies the design of an evolutionary solver by giving population sizing principles and directions to choose the right selection operator. (b) No parameter tuning required to adjust a solver for different environments, introducing robustness. (c) The applied technique is simple and computationally cheap. (d) It boosts the performance of an evolutionary algorithm on high dimensional (constrained and unconstrained) problems. The analysis throughout this book processes an interesting idea which can be useful for those who wish to apply EC to problem solving: specialists, researchers, lecturers, graduate and undergraduate students.
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