AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking

• URL: http://arxiv.org/abs/2010.03917v2
• Date: Mon, 3 May 2021 13:55:03 GMT
• Title: AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking
• Authors: Eneko Osaba, Javier Del Ser, Aritz D. Martinez, Jesus L. Lobo and Francisco Herrera
• Abstract summary: We introduce a novel adaptive metaheuristic algorithm to deal with Evolutionary Multitasking environments. AT-MFCGA relies on cellular automata to implement mechanisms in order to exchange knowledge among the optimization problems under consideration.
• Score: 17.120962133525225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously. Among the different approaches that can address this problem effectively, Evolutionary Multitasking resorts to concepts from Evolutionary Computation to solve multiple problems within a single search process. In this paper we introduce a novel adaptive metaheuristic algorithm to deal with Evolutionary Multitasking environments coined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm (AT-MFCGA). AT-MFCGA relies on cellular automata to implement mechanisms in order to exchange knowledge among the optimization problems under consideration. Furthermore, our approach is able to explain by itself the synergies among tasks that were encountered and exploited during the search, which helps us to understand interactions between related optimization tasks. A comprehensive experimental setup is designed to assess and compare the performance of AT-MFCGA to that of other renowned evolutionary multitasking alternatives (MFEA and MFEA-II). Experiments comprise 11 multitasking scenarios composed of 20 instances of 4 combinatorial optimization problems, yielding the largest discrete multitasking environment solved to date. Results are conclusive in regard to the superior quality of solutions provided by AT-MFCGA with respect to the rest of the methods, which are complemented by a quantitative examination of the genetic transferability among tasks throughout the search process.

Related papers

• Towards Multi-Objective High-Dimensional Feature Selection via Evolutionary Multitasking [63.91518180604101]
  This paper develops a novel EMT framework for high-dimensional feature selection problems, namely MO-FSEMT. A task-specific knowledge transfer mechanism is designed to leverage the advantage information of each task, enabling the discovery and effective transmission of high-quality solutions.
  arXiv  Detail & Related papers  (2024-01-03T06:34:39Z)
• Effective Mutation Rate Adaptation through Group Elite Selection [50.88204196504888]
  This paper introduces the Group Elite Selection of Mutation Rates (GESMR) algorithm. GESMR co-evolves a population of solutions and a population of MRs, such that each MR is assigned to a group of solutions. With the same number of function evaluations and with almost no overhead, GESMR converges faster and to better solutions than previous approaches.
  arXiv  Detail & Related papers  (2022-04-11T01:08:26Z)
• Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees [94.72461292387146]
  We propose to reformulate the result diversification problem as a bi-objective search problem, and solve it by a multi-objective evolutionary algorithm (EA) We theoretically prove that the GSEMO can achieve the optimal-time approximation ratio, $1/2$. When the objective function changes dynamically, the GSEMO can maintain this approximation ratio in running time, addressing the open question proposed by Borodin et al.
  arXiv  Detail & Related papers  (2021-10-18T14:00:22Z)
• Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning [89.31889875864599]
  We propose an efficient model-based reinforcement learning algorithm for learning in multi-agent systems. Our main theoretical contributions are the first general regret bounds for model-based reinforcement learning for MFC. We provide a practical parametrization of the core optimization problem.
  arXiv  Detail & Related papers  (2021-07-08T18:01:02Z)
• EOS: a Parallel, Self-Adaptive, Multi-Population Evolutionary Algorithm for Constrained Global Optimization [68.8204255655161]
  EOS is a global optimization algorithm for constrained and unconstrained problems of real-valued variables. It implements a number of improvements to the well-known Differential Evolution (DE) algorithm. Results prove that EOSis capable of achieving increased performance compared to state-of-the-art single-population self-adaptive DE algorithms.
  arXiv  Detail & Related papers  (2020-07-09T10:19:22Z)
• On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking [6.943742860591444]
  This work is focused on the application of the recently proposed Multifactorial Cellular Genetic Algorithm (MFCGA) to the Capacitated Vehicle Routing Problem (CVRP) The contribution of this research is twofold. On the one hand, it is the first application of the MFCGA to the Vehicle Routing Problem family of problems. On the other hand, equally interesting is the second contribution, which is focused on the quantitative analysis of the positive genetic transferability among the problem instances.
  arXiv  Detail & Related papers  (2020-05-11T12:58:00Z)
• dMFEA-II: An Adaptive Multifactorial Evolutionary Algorithm for Permutation-based Discrete Optimization Problems [6.943742860591444]
  We propose the first adaptation of the recently introduced Multifactorial Evolutionary Algorithm II (MFEA-II) to permutation-based discrete environments. The performance of the proposed solver has been assessed over 5 different multitasking setups.
  arXiv  Detail & Related papers  (2020-04-14T14:42:47Z)
• COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking [9.54239662772307]
  We propose a novel algorithmic scheme for dealing with multitasking environments. The proposed approach, coined as Coevolutionary Bat Algorithm, finds its inspiration in concepts from both co-evolutionary strategies and the metaheuristic Bat Algorithm.
  arXiv  Detail & Related papers  (2020-03-24T13:37:43Z)
• Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithmic Design, Performance Comparison and Genetic Transferability Analysis [17.120962133525225]
  Multiobjective optimization is an incipient research area which is lately gaining a notable research momentum. In this work we propose a novel algorithmic scheme for Multifactorial Optimization scenarios. The proposed MFCGA hinges on concepts from Cellular Automata to implement mechanisms for exchanging knowledge among problems.
  arXiv  Detail & Related papers  (2020-03-24T11:03:55Z)
• Pareto Multi-Task Learning [53.90732663046125]
  Multi-task learning is a powerful method for solving multiple correlated tasks simultaneously. It is often impossible to find one single solution to optimize all the tasks, since different tasks might conflict with each other. Recently, a novel method is proposed to find one single Pareto optimal solution with good trade-off among different tasks by casting multi-task learning as multiobjective optimization.
  arXiv  Detail & Related papers  (2019-12-30T08:58:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.