Titlebook: Evolutionary Computations; New Algorithms and t Keigo Watanabe,M. M. A. Hashem Book 2004 Springer-Verlag Berlin Heidelberg 2004 Normal.algo

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https://doi.org/10.1057/9780230102002ory error, one must find a trade-off between them. To alleviate this trade-off, it is possible to tune stable-optimal gains automatically by constructing an appropriate fitness function using evolutionary algorithms [61]. Consequently, it will provide the controller design process with an automatic

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Evolutionary Algorithms: Revisited,ry difficult to solve. In order to understand the difficulties it is important to note that all local optimization techniques can at most locate a local minimum. Moreover, there is no local criterion to decide whether a local solution is also the global solution. Furthermore, locally optimal solutio

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A Novel Evolution Strategy Algorithm,perseding traditional search techniques. However, they are not without their limitations. In particular, the choice of a good evolutionary operator can make a considerable difference to the exploration and exploitation, and often even the feasibility of the evolutionary search. Moreover, the success

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Evolutionary Optimization of Constrained Problems,ained optimization problems. Constrained optimization problems present the difficulties with potentially nonconvex or even disjoint feasible regions. Classic linear programming and nonlinear programming methods are often either unsuitable or impractical when applied to these constrained problems [76

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Evolutionary Solution of Optimal Control Problems,f the controlled system is described by a set of state variables. The transition from one state to the next is achieved through a set of controlled variables. The choice of the values for these controlled variables so as to cause the system to move towards the specified target state constitutes the

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Evolutionary Behavior-Based Control of Mobile Robots, robots [68], in which a population is divided automatically into several subgroups according to a similarity of each individual, the SBMAC is used in the crossover operation, and the standard deviation calculated for each objective variable within each subgroup at a generation is applied for the mu