Rotationally Invariant Crossover Operators in Evolutionary Multi-objective Optimization

Iorio, Antony W.; Li, Xiaodong

doi:10.1007/11903697_40

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…As a result of the above problem, pure DE has better performance than pure GA. However, reference [19] shows that GA with the simplex crossover (SPX) [20] reaches better performance than DE regarding the convergence metric. Therefore, the difference of the capability regarding the crossover in GA has been investigated on the hybrid method between GA and DE.…”

Section: Capability Of Crossover On Hybrid Methods Between Ga and Dementioning

confidence: 99%

Evolutionary-Based Hybrid Optimizer Applicable to Large-Scale Design Problems

Chiba

2013

JCST

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Design Informatics has three points of view. First point is the efficient exploration in design space using evolutionary computation. Second point is the structuring and visualizing of design space using data mining. Third point is the application to practical problems. The investigation of efficient evolutionary-based optimizer for the above first point is essential in order to generate hypothetical database for data mining. In the present study, the performance regarding diversity and convergence has been compared among pure and their hybrid methods using three standard mathematical test problems with/without noise. The result indicates that the hybrid method between the genetic algorithm based on the elitist non-dominated sorting genetic algorithm and the differential evolution is better performance for efficient exploration in the design space under the condition for large-scale engineering design problems within 10 2 order evolution at most. Moreover, the comparison among eight crossover indicates that the principal component analysis blended crossover is good performance on the hybrid method between the genetic algorithm and the differential evolution.

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Section: Capability Of Crossover On Hybrid Methods Between Ga and Dementioning

confidence: 99%

Evolutionary-Based Hybrid Optimizer Applicable to Large-Scale Design Problems

Chiba

2013

JCST

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show abstract

“…As a result of the above problem, pure DE has beuer performance than pure GA. However, the reference [24] shows that GA with the simplex crossover(SPX) [25] reaches better performance than DE regarding the convergence metric. Therefore, the difference of the capability regarding the crossover in GA has been investigated on the hybrid GAlDE.…”

Section: Performance Evaluation Of Optimizersmentioning

confidence: 99%

Evolutionary hybrid computation in view of design information by data mining

Chiba

2013

2013 IEEE Congress on Evolutionary Computation

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Design Informatics has three points of view. First point is the effident exploration in design space using evolu tionary computation. Second point is the structurization and visualization of design space using data mining. Third point is the application to practical problems. In the present study, the influence of the seven pure and hybrid optimizers for design information has been investigated in order to explain the selection manner of optimizer for data mining. A single-stage hybrid rocket design problem is picked up as the present design object. As a result, mining result depends on not the number of generation (convergence) but the optimizers (diversity). Consequently, the optimizer with diversity performance should be selected in order to obtain global design information in the design space. Therefore, the diversity performance has also been explained for the seven optimization methods by using three standard mathematical test problems with/without noise. The resuIt indicates that the hybrid method between the differential evolution and the genetic algorithm is benefidal performance for effident exploration in the design space under the condition for large-scale design problems within 102 order evolution at most. Moreover, the comparison among eight crossovers indicates that the prindpal component analysis blended crossover is good selection on the hybrid method between the differential evolution and the genetic algorithm.Index Terms-design informatics, evolutionary hybrid opti mization method, data mining, large-scale practical problem.

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“…In the field of EC, different evolutionary operators show different competence on different problems [3,[167][168][169][170][171]. For instance, the SBX operator does not possess the property of rotational invariance since the correlation between the location of parents is lost under the rotation of a decision space.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, the SBX operator does not possess the property of rotational invariance since the correlation between the location of parents is lost under the rotation of a decision space. Thus, SBX is generally preferable for dealing with MOPs with independent variables [167]. On the contrary, polynomial mutation is suitable for handling MOPs with nonlinear dependent variables [3,168].…”

Section: Related Workmentioning

confidence: 99%

“…MOEAs, the evolutionary operators "SBX" and "polynomial mutation" show great contributions. It is also evident that "SBX" fares better on independent PSs (DTLZ1 and WFG1D3) than on interdependent PSs (LZ09F1 and UF1) [167], whereas, "polynomial mutation" is more suitable to deal with nonlinear variable dependencies than independent variables [168].…”

Section: Effect Of Pareto Sets On Operator Selectionmentioning

confidence: 99%

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Multiagent evolutionary computation for complex problems

Jiang¹

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Multiagent evolutionary computation (MAEC) is a new paradigm to efficiently solve a range of complex problems, by combining the advantages of evolutionary computation (EC) and multiagent systems (MAS). In general, there are three categories in MAEC: 1) "agent based EC" incorporates characteristics of intelligent agents, and adopts techniques in MAS to enhance the performance of EC; 2) "evolutionary computation based MAS" utilizes evolutionary techniques to design self-learning agents, to improve the efficiency of interactions between agents, and to increase the effectiveness of the whole system; 3) I would like to express my sincere thanks to my supervisors Prof. Jie Zhang and Prof. Yew-Soon Ong from School of Computer Engineering, who have continuously encouraged, inspired and supported me throughout my Ph.D. research career. My immense gratitude goes to my wife (Ms. Dan Zeng), my parents and my two elder sisters for their great love and continuous encouragement in both my study and life. I also want to thank my colleagues, Ms.

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Rotationally Invariant Crossover Operators in Evolutionary Multi-objective Optimization

Cited by 10 publications

References 14 publications

Evolutionary-Based Hybrid Optimizer Applicable to Large-Scale Design Problems

Evolutionary-Based Hybrid Optimizer Applicable to Large-Scale Design Problems

Evolutionary hybrid computation in view of design information by data mining

Multiagent evolutionary computation for complex problems

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