evolutionary algorithms
Optimization of coefficients of lists of polynomials by evolutionary algorithms
... the gcd (greatest common divisor) of all coefficients of the polynomials, the resulting integers are minimized in absolute value. Evolution strategies, a special class of heuristic, evolutionary algorithms, ...
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Evolutionary algorithms to face computer systems management problems
... As the model assumes independence of processes the goal of the strategy is to minimize the mean time of processes in the system. This performance variable will be called mean response time. Dynamic load balancing ...
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Sensor resource management with evolutionary algorithms applied to indoor positioning
... multi-objective evolutionary algorithms that optimize several metrics of the covariance of the estimation, such as average mean squared error in the area, isotropy of the solution, or the maximum deviation ...
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Solving unrestricted parallel machine scheduling problems via evolutionary algorithms
... of the most important objective functions to be minimized, because it usually implies high utilization of resources, but other important objectives must be also considered. These problems are known in the literature [9, ...
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Parallel evolutionary algorithms in telecommunications: two case studies
... parallel evolutionary algorithms (EAs) are developed and evaluated on two hard optimisation problems arising in the field of Telecommunications: designing error correcting codes, and finding optimal ...
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An efficient constraint handling methodology for multi-objective evolutionary algorithms
... Evolutionary algorithms (EA) have been widely used in the solution of optimization problems. These techniques, compared with the traditional nonlinear programming methods, handle a smaller amount of ...
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Improving evolutionary algorithms performance by extending incest prevention
... Provision of population diversity is one of the main goals to avoid premature convergence in Evolutionary Algorithms (EAs). In this way the risk of being trapped in local optima is minimised. Eshelman and ...
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A self-adaptive recombination method in evolutionary algorithms for solving epistatic problems
... adaptive evolutionary algorithm to solve the NK landscape problem, which dynamically selects the recombination operator from an operator pool during the evolution; this removes the need of specifying a single ...
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Enhancing evolutionary algorithms through recombination and parallelism
... of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most traditional approaches which improve a single ...these algorithms ...
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Enhancing evolutionary algorithms through recombination and parallelism
... of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most traditional approaches which improve a single ...these algorithms ...
12
Sensor resource management with evolutionary algorithms applied to indoor positioning
... multi-objective evolutionary algorithms that optimize several metrics of the covariance of the estimation, such as average mean squared error in the area, isotropy of the solution, or the maximum deviation ...
114
Solving the two dimensional cutting problem using evolutionary algorithms with penalty functions
... using evolutionary algorithms with penalty function for the non-guillotine cutting problem is ...resulting algorithms are carried out using publicly available benchmarks to the non-guillotine cutting ...
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