We propose real-coded genetic algorithms that utilize a method for detecting dependency relationships betweenvariables. The method consists of neural network regression and group lasso. The proposed genetic algorithm...
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ISBN:
(纸本)9781728197326
We propose real-coded genetic algorithms that utilize a method for detecting dependency relationships betweenvariables. The method consists of neural network regression and group lasso. The proposed genetic algorithms select an appropriate crossover operator based on dependency information betweenvariables, which are obtained from past solution candidates. Simulation results using the CEC'13 benchmark functions show that the proposed algorithms outperform conventional real-coded genetic algorithms.
Conventional evolutionary algorithms (EAs) cannot solve given optimization problems efficiently when their evolutionary operators do not accommodate to the structures of the problems. We previously proposed a mutation...
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ISBN:
(纸本)9783319135632;9783319135625
Conventional evolutionary algorithms (EAs) cannot solve given optimization problems efficiently when their evolutionary operators do not accommodate to the structures of the problems. We previously proposed a mutation-based EA that does not use a recombination operator and does not have this problem of the conventional EAs. The mutation-based EA evolves timings at which probabilities for generating phenotypic values (developmental timings) change, and brings different evolution speed to each phenotypic variable, so that it can solve a given problem hierarchically. In this paper we first propose the evolutionary algorithm evolving developmental timing (EDT) by adding a crossover operator to the mutation-based EA and then devise a new test problem that conventional EAs are likely to fail in solving and for which the features of the proposed EA are well utilized. The test problem consists of multiple deceptive problems among which there is hierarchical dependency, and has the feature that the hierarchical dependency is represented by a graph structure. We apply the EDT and the conventional EAs, the PBIL and cGA, for comparison to the new test problem and show the usefulness of the evolution of developmental timing.
We respond to the comment by Oliveira-Santos et al. (2013) on the article on the suitability of distance metrics as indexes of home-range size by Puttker et al. (2012). We argue that geometrical relationships between ...
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We respond to the comment by Oliveira-Santos et al. (2013) on the article on the suitability of distance metrics as indexes of home-range size by Puttker et al. (2012). We argue that geometrical relationships between distances and area are not an artifact, but 1st principles that warrant the use of movement distances as indexes for home-range area. Indeed, the simulations provided by Oliveira-Santos et al. corroborate this view. Although we agree that the use of minimum convex polygons (MCPs) based on trapping data as estimates of home-range size requires confirmation, this was beyond the scope of our study, which centered on the relationship of distance and area for a given method (MCP) and field protocol (trapping). Moreover, the analyses of Oliveira-Santos et al. testing the relationship between distance metrics (obtained by trapping) and area (estimated by radiotelemetry) are of limited utility due to confounding factors related to differences in field methods and time interval considered to obtain the 2 estimates (distance and area), and the inadequate size of their trapping grids for estimating movement distances.
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