A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming

Hong, Libin; Drake, John H.; Woodward, John R.; Özcan, Ender

doi:10.1016/j.asoc.2017.10.002

Cited by 36 publications

(18 citation statements)

References 36 publications

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“…Concerning the evolution of operators, the authors of [10] show how they evolved a general purpose mutation operator for Evolutionary Programming which outperforms existing operators on classes of functions (i.e., problems); they also experimentally show that a mutation operator evolved for a specific problem is better than a general purpose evolved operator. A similar goal is aimed at in [6], where a framework for the online evolution of the operators, together with the solutions, is proposed: as in the previously cited work, operators are represented as trees and evolved using GP.…”

Section: Related Workmentioning

confidence: 99%

Designing automatically a representation for grammatical evolution

Medvet

Bartoli

Lorenzo

et al. 2018

Genet Program Evolvable Mach

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A long-standing problem in Evolutionary Computation consists in how to choose an appropriate representation for the solutions. In this work we investigate the feasibility of synthesizing a representation automatically, for the large class of problems whose solution spaces can be defined by a context-free grammar. We propose a framework based on a form of meta-evolution in which individuals are candidate representations expressed with an ad hoc language that we have developed to this purpose. Individuals compete and evolve according to an evolutionary search aimed at optimizing such representation properties as redundancy, uniformity of redundancy, and locality. We assessed experimentally three variants of our framework on established benchmark problems and compared the resulting representations to human-designed representations commonly used (e.g., classical Grammatical Evolution). The results are promising as the evolved representations indeed exhibit better properties than the human-designed ones. Furthermore, the evolved representations compare favorably with the human-designed baselines in search effectiveness as well. Specifically, we select a best evolved representation as the representation with best search effectiveness on a set of learning problems and assess its effectiveness on a separate set of challenging validation problems. For each of the three proposed variants of our framework, the best evolved representation exhibits an average fitness rank on the set of validation problems that is better than the average fitness rank of the human-designed baselines on the same problems.

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Section: Related Workmentioning

confidence: 99%

Designing automatically a representation for grammatical evolution

Medvet

Bartoli

Lorenzo

et al. 2018

Genet Program Evolvable Mach

View full text Add to dashboard Cite

show abstract

“…Concerning the evolution of operators, the authors of [8] show how they evolved a general purpose mutation operator for Evolutionary Programming which outperforms existing operators on classes of functions (i.e., problems); they also experimentally show that a mutation operator evolved for a specific problem is better than a general purpose evolved operator. A similar goal is aimed at in [4], where a framework for the online evolution of the operators, together with the solutions, is proposed: as in the previously cited work, operators are represented as trees and evolved using GP.…”

Section: Related Workmentioning

confidence: 99%

On the Automatic Design of a Representation for Grammar-Based Genetic Programming

Medvet

Bartoli

2018

Lecture Notes in Computer Science

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A long-standing problem in Evolutionary Computation consists in how to choose an appropriate representation for the solutions. In this work we investigate the feasibility of synthesizing a representation automatically, for the large class of problems whose solution spaces can be defined by a context-free grammar. We propose a framework based on a form of meta-evolution in which individuals are candidate representations expressed with an ad hoc language that we have developed to this purpose. Individuals compete and evolve according to an evolutionary search aimed at optimizing such representation properties as redundancy, locality, uniformity of redundancy. We assessed experimentally three variants of our framework on established benchmark problems and compared the resulting representations to human-designed representations commonly used (e.g., classical Grammatical Evolution). The results are promising in the sense that the evolved representations indeed exhibit better properties than the humandesigned ones. Furthermore, while those improved properties do not result in a systematic improvement of search effectiveness, some of the evolved representations do improve search effectiveness over the humandesigned baseline.

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“…GPHH is a popular HH approach and has been widely used in many applications because of its flexible representation and excellent search ability. Existing works have used GPHH to automatically generate mutation operators (Hong et al, 2018), optimise the running time of software (Langdon and Harman, 2015), generate the motion feature descriptor in a feature extraction method and design diverse classifiers with selected features (Nag and Pal, 2016). According to the previous work, GPHH can not only solve optimisation problems, but also be embedded in the other search approaches to improve their performance.…”

Section: Gphh Methods For Dfjssmentioning

confidence: 99%

Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling

Mei

Wang

et al. 2021

Evolutionary Computation

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Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and can have multiple conflicting objectives. Genetic Programming Hyper-Heuristic (GPHH) is a promising approach to fast respond to the dynamic and unpredictable events in DFJSS. A GPHH algorithm evolves dispatching rules (DRs) that are used to make decisions during the scheduling process (i.e. the so-called heuristic template). In DFJSS, there are two kinds of scheduling decisions: the routing decision that allocates each operation to a machine to process it, and the sequencing decision that selects the next job to be processed by each idle machine. The traditional heuristic template makes both routing and sequencing decisions in a non-delay manner, which may have limitations in handling the dynamic environment. In this paper, we propose a novel heuristic template that delays the routing decisions rather than making them immediately. This way, all the decisions can be made under the latest and more accurate information. We propose three different delayed routing strategies, and automatically evolve the rules in the heuristic template by GPHH. We evaluate the newly proposed GPHH with Delayed Routing (GPHH-DR) on a multi-objective DFJSS that optimises the energy efficiency and mean tardiness. The experimental results show that GPHH-DR significantly outperformed the state-of-the-art GPHH methods. We further demonstrated the efficacy of the proposed heuristic template with delayed routing, which suggests the importance of delaying the routing decisions.

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A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming

Cited by 36 publications

References 36 publications

Designing automatically a representation for grammatical evolution

Designing automatically a representation for grammatical evolution

On the Automatic Design of a Representation for Grammar-Based Genetic Programming

Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling

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