A New Efficient Entropy Population-Merging Parallel Model for Evolutionary Algorithms

Arellano‐Verdejo, Javier; Godoy-Calderón, Salvador; Alonso-Pecina, Federico; Arenas, Adolfo Guzmáan; Cruz-Chávez, Marco Antonio

doi:10.2991/ijcis.10.1.78

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…Two real-world smart factory optimization scenarios are used as test cases, and the system is deployed on a Kubernetes cluster. This work follows a novel approach proposed by Arellano-Verdejo et al [39], in which islands evolve, but there is no migration between them.…”

Section: State Of the Artmentioning

confidence: 99%

Distributed and Asynchronous Population-Based Optimization Applied to the Optimal Design of Fuzzy Controllers

et al. 2023

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Designing a controller is typically an iterative process during which engineers must assess the performance of a design through time-consuming simulations; this becomes even more burdensome when using a population-based metaheuristic that evaluates every member of the population. Distributed algorithms can mitigate this issue, but these come with their own challenges. This is why, in this work, we propose a distributed and asynchronous bio-inspired algorithm to execute the simulations in parallel, using a multi-population multi-algorithmic approach. Following a cloud-native pattern, isolated populations interact asynchronously using a distributed message queue, which avoids idle cycles when waiting for other nodes to synchronize. The proposed algorithm can mix different metaheuristics, one for each population, first because it is possible and second because it can help keep total diversity high. To validate the speedup benefit of our proposal, we optimize the membership functions of a fuzzy controller for the trajectory tracking of a mobile autonomous robot using distributed versions of genetic algorithms, particle swarm optimization, and a mixed-metaheuristic configuration. We compare sequential versus distributed implementations and demonstrate the benefits of mixing the populations with distinct metaheuristics. We also propose a simple migration strategy that delivers satisfactory results. Moreover, we compare homogeneous and heterogenous configurations for the populations’ parameters. The results show that even when we use random heterogeneous parameter configuration in the distributed populations, we obtain an error similar to that in other work while significantly reducing the execution time.

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Section: State Of the Artmentioning

confidence: 99%

Distributed and Asynchronous Population-Based Optimization Applied to the Optimal Design of Fuzzy Controllers

et al. 2023

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“…There are multiple choices of the migration strategies [20] since islands can be clustered together using various similarity measures, either according to their fitness values, or through other measures of diversity like entropy [21], or even dynamically through spectral clustering based on the pair-wise similarities between individuals [22]. In general, having more densely connected islands gives a higher accuracy of the lower bound, but it is more computationally expensive.…”

Section: B Maintaining Diversitymentioning

confidence: 99%

Automated Graph Genetic Algorithm based Puzzle Validation for Faster Game Design

Levonyan

Harder

Silva

2022

2022 IEEE Congress on Evolutionary Computation (CEC)

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Many games are reliant on creating new and engaging content constantly to maintain the interest of their playerbase. One such example are puzzle games, in such it is common to have a recurrent need to create new puzzles. Creating new puzzles requires guaranteeing that they are solvable and interesting to players, both of which require significant time from the designers. Automatic validation of puzzles provides designers with a significant time saving and potential boost in quality. Automation allows puzzle designers to estimate different properties, increase the variety of constraints, and even personalize puzzles to specific players. Puzzles often have a large design space, which renders exhaustive search approaches infeasible, if they require significant time. Specifically, those puzzles can be formulated as quadratic combinatorial optimization problems. This paper presents an evolutionary algorithm, empowered by expert-knowledge informed heuristics, for solving logical puzzles in video games efficiently, leading to a more efficient design process. We discuss multiple variations of hybrid genetic approaches for constraint satisfaction problems that allow us to find a diverse set of near-optimal solutions for puzzles. We demonstrate our approach on a fantasy Party Building Puzzle game, and discuss how it can be applied more broadly to other puzzles to guide designers in their creative process.

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“…A Population-Merging Parallel Model for Evolutionary Algorithms has been proposed in [1]. In that model, a predefined number of islands is created at the beginning of the optimisation process.…”

Section: Related Workmentioning

confidence: 99%

“…The computational power scales automatically, is highly available and fault tolerant. The first request can initially see several seconds response time but is shorter than 1s for the subsequent requests 1 . Using such services is also economically beneficial as there is no charge for the time when code is not running.…”

Section: Introductionmentioning

confidence: 99%

Scalable distributed evolutionary algorithm orchestration using Docker containers

Dziurzański

Zhao

Przewoźniczek

et al. 2020

Journal of Computational Science

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In smart factories, integrated optimisation of manufacturing process planning and scheduling leads to better results than a traditional sequential approach but is computationally more expensive and thus difficult to be applied to real-world manufacturing scenarios. In this paper, a working approach for cloud-based distributed optimisation for process planning and scheduling is presented. Three managers dynamically governing the creation and deletion of subpopulations (islands) evolved by a multi-objective genetic algorithm are proposed, compared and contrasted. A number of test cases based on two real-world manufacturing scenarios are used to show the applicability of the proposed solution.

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A New Efficient Entropy Population-Merging Parallel Model for Evolutionary Algorithms

Cited by 7 publications

References 20 publications

Distributed and Asynchronous Population-Based Optimization Applied to the Optimal Design of Fuzzy Controllers

Distributed and Asynchronous Population-Based Optimization Applied to the Optimal Design of Fuzzy Controllers

Automated Graph Genetic Algorithm based Puzzle Validation for Faster Game Design

Scalable distributed evolutionary algorithm orchestration using Docker containers

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