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12th Italian Workshop on Artificial Life and evolutionary Computation (WIVACE)

作者： Baioletti, Marco Milani, Alfredo Santucci, Valentino Univ Perugia Dept Math & Comp Sci Perugia Italy Hong Kong Baptist Univ Dept Comp Sci Kowloon Tong Hong Kong Peoples R China

ISBN: (纸本)9783319786582;9783319786575

Motivated from the previously proposed algebraic framework for combinatorial optimization, here we introduce a novel formal languages-based perspective on discrete search spaces that allows to automatically derive algebraic evolutionary algorithms. The practical effect of the proposed approach is that the algorithm designer does not need to choose a solutions encoding and implement algorithmic procedures. Indeed, he/she only has to provide the group presentation of the discrete solutions of the problem at hand. Then, the proposed mechanism allows to automatically derive concrete implementations of a chosen evolutionary algorithms. Theoretical guarantees about the feasibility of the proposed approach are provided.

关键词： algebraic evolutionary algorithms Combinatorial optimization Formal language perspective

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An algebraic framework for swarm and evolutionary algorithms in combinatorial optimization

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SWARM AND evolutionary COMPUTATION 2020年 55卷 100673-100673页

作者： Santucci, Valentino Baioletti, Marco Milani, Alfredo Univ Foreigners Perugia Dept Humanities & Social Sci Perugia Italy Univ Perugia Dept Math & Comp Sci Perugia Italy

A popular trend in evolutionary computation is to adapt numerical algorithms to combinatorial optimization problems. For instance, this is the case of a variety of Particle Swann Optimization and Differential Evolution implementations for both binary and permutation-based optimization problems. In this paper, after highlighting the main drawbacks of the approaches in literature, we provide an algebraic framework which allows to derive fully discrete variants of a large class of numerical evolutionary algorithms to tackle many combinatorial problems. The strong mathematical foundations upon which the framework is built allow to redefine numerical evolutionary operators in such a way that their original movements in the continuous space are simulated in the discrete space. algebraic implementations of Differential Evolution and Particle Swarm Optimization are then proposed. Experiments have been held to compare the algebraic algorithms to the most popular schemes in literature and to the state-of-the-art results for the tackled problems. Experimental results clearly show that algebraic algorithms outperform the competitors and are competitive with the state-of-the-art results.

关键词： algebraic evolutionary algorithms Combinatorial search spaces algebraic evolutionary computation

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MOEA/DEP: An algebraic Decomposition-Based evolutionary Algorithm for the Multiobjective Permutation Flowshop Scheduling Problem 1

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18th European Conference on evolutionary Computation in Combinatorial Optimization (EvoCOP)

作者： Baioletti, Marco Milani, Alfredo Santucci, Valentino Univ Perugia Dept Math & Comp Sci Perugia Italy Hong Kong Baptist Univ Dept Comp Sci Kowloon Tong Hong Kong Peoples R China

ISBN: (数字)9783319774497

ISBN: (纸本)9783319774497;9783319774480

algebraic evolutionary algorithms are an emerging class of meta-heuristics for combinatorial optimization based on strong mathematical foundations. In this paper we introduce a decomposition-based algebraic evolutionary algorithm, namely MOEA/DEP, in order to deal with multiobjective permutation-based optimization problems. As a case of study, MOEA/DEP has been experimentally validated on a multiobjective permutation flowshop scheduling problem (MoPFSP). In particular, the makespan and total flowtime objectives have been investigated. Experiments have been held on a widely used benchmark suite, and the obtained results have been compared with respect to the state-of-the-art Pareto fronts for MoPFSP. The experimental results have been analyzed by means of two commonly used performance metrics for multiobjective optimization. The analysis clearly shows that MOEA/DEP reaches new state-of-the-art results for the considered benchmark.

关键词： algebraic evolutionary algorithms Multiobjective optimization Permutation Flowshop Scheduling Problem

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