The Pareto fitness genetic algorithm: Test function study

Elaoud, Semya; Loukil, Taïcir; Teghem, Jacques

doi:10.1016/j.ejor.2005.10.018

Cited by 48 publications

(31 citation statements)

References 15 publications

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“…This method relies on the Population-size Adapted Density Estimation (PADE) procedure suggested by Elaoud et al (2007). PADE is a local density computation scheme in which a dynamic hypergrid is automatically adapted to the population spread in the criteria space.…”

Section: Preserving Diversitymentioning

confidence: 99%

Solving the musical orchestration problem using multiobjective constrained optimization with a genetic local search approach

2009

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In this paper a computational approach of musical orchestration is presented. We consider orchestration as the search of relevant sound combinations within large instruments sample databases and propose two cooperating metaheuristics to solve this problem. Orchestration is seen here as a particular case of finding optimal constrained multisets on a large ensemble with respect to several objectives. We suggest a generic and easily extendible formalization of orchestration as a constrained multiobjective search towards a target timbre, in which several perceptual dimensions are jointly optimized. We introduce Orchidée, a time-efficient evolutionary orchestration algorithm that allows the discovery of optimal solutions and favors the exploration of non-intuitive sound mixtures. We also define a formal framework for global constraints specification and introduce the innovative CDCSolver repair metaheuristic, thanks to which the search is led towards regions fulfilling a set of musical-related requirements. Evaluation of our approach on a wide set of real orchestration problems is also provided.

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Section: Preserving Diversitymentioning

confidence: 99%

Solving the musical orchestration problem using multiobjective constrained optimization with a genetic local search approach

2009

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“…According to this, two objectives are defined, namely Fitness minrisk and Fitness maxrenta . A priori, it could be thought that the suitable Fitness is a Pareto multiobjective evaluation function, [8,9]. 5 However, the preliminary tests carried out showed a divergent evolution of both objectives, obtaining high return and high levels of risk.…”

Section: Descriptionmentioning

confidence: 99%

“…In Sect. 3.5 Fitness return employing 9 Equivalent to an annual 2% obtained through a riskfree asset, such as for example, a fixed rate deposit. 10 It refers to the habitual training used in the literature which consists in always using the same trading rule for the whole test period.…”

Section: Summary Of the Tests Carried Outmentioning

confidence: 99%

Improving return using risk-return adjustment and incremental training in technical trading rules with GAPs

2008

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The principle objective of this paper is to obtain trading rules with a low risk level which are also capable of obtaining high returns. To that purpose a methodology has been defined, based on the design of a genetic algorithm GAP and an incremental training technique adapted to the learning of series of stock market values. The GAP technique consists in a fusion of GP and GA. In GAP a chromosome is composed of a tree with language operators and a vector with numeric values. The GAP algorithm implements the automatic search for trading rules taking as objectives of the training both the optimization of the return obtained and the minimization of the assumed risk. In order to diminish high over-fitting, a technique of incremental training has been used. Applying the proposed methodology, rules have been obtained for a period of eight years of the S&P500 index. The achieved adjustment of the relation return-risk has generated rules with returns very superior in the testing period to those obtained applying habitual methodologies and even clearly superior to Buy&Hold. Insert your abstract here. Include keywords, PACS and mathematical subject classification numbers as needed.

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“…In 2007, Campelo et al [10] suggested that the negative selection, danger theory and other immune mechanisms may improve the existing MOIAs. In the same year, Elaoud et al proposed Pareto fitness genetic algorithm (PFGA) [11]. It modified ranking procedure and possessed a promising way of sharing.…”

Section: Related Workmentioning

confidence: 99%

An Improved Multi-Objective Genetic Algorithm for Solving Multi-objective Problems

Hsieh¹,

Chiu²,

Yen³

2013

Appl. Math. Inf. Sci.

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Multi-objective optimization (MO) has been an active area of research in the last two decades. In multi-objective genetic algorithm (MOGA), the quality of newly generated offspring of the population will directly affect the performance of finding the Pareto optimum. In this paper, an improved MOGA, named SMGA, is proposed for solving multi-objective optimization problems. To increase efficiency during solution searching, an effective mutation named sharing mutation is adopted to generate potential offspring. Experiments were conducted on CEC-09 MOP test problems. The results show that the proposed method exhibits better performance when solving these benchmark problems compared to related multi-objective evolutionary algorithms (MOEA).

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The Pareto fitness genetic algorithm: Test function study

Cited by 48 publications

References 15 publications

Solving the musical orchestration problem using multiobjective constrained optimization with a genetic local search approach

Solving the musical orchestration problem using multiobjective constrained optimization with a genetic local search approach

Improving return using risk-return adjustment and incremental training in technical trading rules with GAPs

An Improved Multi-Objective Genetic Algorithm for Solving Multi-objective Problems

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