Parametric Analysis of Iterated Game Environments as Social Interaction Model for Genetic Algorithm to Solve Constrained Engineering Problems

Pereira, Rodrigo Lisbôa; Mollinetti, Marco Antonio Florenzano; Neto, Mario Tasso Ribeiro Serra; Neto, Adilson de Almeida; Souza, Daniel Leal; Yasojima, Edson Koiti Kudo; Teixeira, Otávio Noura; Oliveira, Roberto Célio Limão de

doi:10.1109/cec.2018.8477925

Cited by 2 publications

(6 citation statements)

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“…This article extends the study shown in [10], where it was presented a new mechanism of selective pressure's selfadaptive control using GT in the selection phase (GASI), as well as two new crossover methods, named GBX and GBX2. The crossovers' dispute environment is modeled from the Prisoner's Dilemma paradigm, the most famous and studied game from the field of GT.…”

Section: A Problem Discussionsupporting

confidence: 60%

“…As said early, this paper is a continuation of the study published by Pereira et al [10], who conducted an in-depth analysis from the influence of four dispute configurations (Hawk-Dove, Stag Hunt, Harmonic Games and Prisoner's Dilemma) to the GA's selection operator aiming to solve problems of the following form:…”

Section: Related Workmentioning

confidence: 95%

“…The offspring use the payoff values of the Social Interaction multiplied by a linear growth function θ (31) based on the number of t iterations bounded by t max . This function is similar to the linear weighting functions (9) and (10). Equation (31) forces solutions to explore solutions that are closer to the global best in early executions of GBEM mode, increasing the intensification of the search in later stages.…”

Section: ) Global Best Exploitation Mode (Gbem)mentioning

confidence: 99%

“…Another essential modification is in the calculation of lower and upper bounds of the GBX2. A fixed value is due VOLUME X, XXXX by the subtraction of X [j] M AX and X [j] M IN 10 . Equations (35) to (38) show the T, R, P, S estimations for GBX2.…”

Section: A An Alternative Version Of Gbx (Gbx2)mentioning

confidence: 99%

“…A common practice is combining GA's with other heuristics or exact methods, such as local or global search procedures to enhance features of GA's to solve problems of more complex domains [4]- [9]. A promising direction for the hybridization of GA is the incorporation of aspects of Game Theory (GT) combining them to new approaches together with pre-established canonical GA mechanisms [10].…”

Section: Introductionmentioning

confidence: 99%

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Game Theory and Social Interaction for Selection and Crossover Pressure Control in Genetic Algorithms: An Empirical Analysis to Real-Valued Constrained Optimization

et al. 2020

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Game Theory (GT) formalizes dispute scenarios between two or more players where each one makes a move following their strategy profiles. The following paper introduces the integration of GT to selection and crossover steps of Genetic Algorithms as an evolutionary model of the representation of population in a similar way to human social evolution. Two ideas are proposed to be incorporated into the GA. First, the Genetic Algorithm with Social Interaction (GASI), a family of GAs that uses GT in selection phase to increase the diversification of the solutions. Second, the (Game-Based Crossover) GBX and GBX2 crossover operators, competition-based tournament selection methods that employ social dispute to generate more diverse offspring. Performance and robustness of the new approaches were assessed by ten continuous and constrained engineering design optimization problems and compared against variants of the canonical GA, as well as well-known heuristics from the literature. Results indicate significant performance relevance in most instances compared to other algorithms and highlight the benefits of combining GT and GA.

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Section: A Problem Discussionsupporting

confidence: 60%

Section: Related Workmentioning

confidence: 95%

Section: ) Global Best Exploitation Mode (Gbem)mentioning

confidence: 99%

Section: A An Alternative Version Of Gbx (Gbx2)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Game Theory and Social Interaction for Selection and Crossover Pressure Control in Genetic Algorithms: An Empirical Analysis to Real-Valued Constrained Optimization

et al. 2020

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Dynamic Interconnection Approach With BLX-Based Search Applied to Multi-Swarm Optimizer: An Empirical Analysis to Real Constrained Optimization

et al. 2023

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The Multi-Swarm approach allows the use of multiple configurations between two or more populations of particles, where each one can present different approaches (e.g. lbest, gbest, Unified, Guaranteed-Convergence) directed towards improving the optimization process. This article presents a proposal for local/global stochastic interconnection applied to the context of the Multi-Swarm algorithm, as well as for incrementing a local search method for refining previously obtained solutions. Two proposals are introduced for this new Multi-Swarm PSO (MSO). The first one is the inclusion of "counterpart particles", which establishes a sub-topology between inter-swarm particles, accessed by migration and evaluability rules. The other involves using customized crossover operators and is based on the BLX scheme (Blend Crossover) with direction information used as a reference for establish a subspace search around the particles. Performance and robustness of the new approaches were assessed by ten constrained engineering design optimization problems (COPs), as is compared to other solutions already published in the scientific literature. Results indicate significant performance improvements for all 10 COPs when compared to concurrent-based MSOs. By making available new references from other swarms, the counterpart particles approach tends to improve the optimization process in the search space, while an intermediate layer of local search based on a modified directed BLX crossover should provide an extra search around the particle, and thus, refining previously obtained solutions.

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Parametric Analysis of Iterated Game Environments as Social Interaction Model for Genetic Algorithm to Solve Constrained Engineering Problems

Cited by 2 publications

References 9 publications

Game Theory and Social Interaction for Selection and Crossover Pressure Control in Genetic Algorithms: An Empirical Analysis to Real-Valued Constrained Optimization

Game Theory and Social Interaction for Selection and Crossover Pressure Control in Genetic Algorithms: An Empirical Analysis to Real-Valued Constrained Optimization

Dynamic Interconnection Approach With BLX-Based Search Applied to Multi-Swarm Optimizer: An Empirical Analysis to Real Constrained Optimization

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