2014
DOI: 10.1016/j.neucom.2012.09.045
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Towards an efficient genetic algorithm optimizer for sequential projection pursuit

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Cited by 26 publications
(18 citation statements)
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“…Further developments focused on developing more global methods for PP optimization, such as random search [38,39,29], genetic algorithm (GA) [32], random scan sampling algorithm (RSSA) [34], simulated annealing (SA) [21], particle swarm optimization (PSO) [35] and tribes [40]. In a previous work [33] we describe PPGA, a GA optimizer with a specialized crossover operator that often showed to find solutions better than those found by PSO, RSSA, and SA when used inside the SPP framework, reason why it is adopted for the present work.…”
Section: Pp Optimizationmentioning
confidence: 99%
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“…Further developments focused on developing more global methods for PP optimization, such as random search [38,39,29], genetic algorithm (GA) [32], random scan sampling algorithm (RSSA) [34], simulated annealing (SA) [21], particle swarm optimization (PSO) [35] and tribes [40]. In a previous work [33] we describe PPGA, a GA optimizer with a specialized crossover operator that often showed to find solutions better than those found by PSO, RSSA, and SA when used inside the SPP framework, reason why it is adopted for the present work.…”
Section: Pp Optimizationmentioning
confidence: 99%
“…In the experiments, the size (w) of the initial population is set as w¼ 3q, formed by 50% of individuals from CPS and 50% created randomly. Projection pursuit genetic algorithm (PPGA): This is the adopted PP optimizer, taken from our previous work [33]. Unlike the original GA optimizer, which uses binary encoding and canonical operators, PPGA uses a real encoding and a specialized crossover operator (called the Inner-outer Hypercone crossover).…”
Section: Pp Stagementioning
confidence: 99%
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