Differential evolution ensemble designer

A meta-evolutionary framework called Differential Evolution Ensemble Designer (DEED) has been proposed in this paper to automate the design of DE ensemble algorithms. Given the design components of DE ensembles and a set of optimization problems, DEED evolves effective and robust DE ensemble designs...

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Vydáno v:Expert systems with applications Ročník 238; s. 121674
Hlavní autoři: M.T., Indu, C., Shunmuga Velayutham
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.03.2024
Témata:
Automated algorithm design
Ensemble differential evolution
Grammatical evolution
Meta evolutionary approach
Parameter tuning
Grammatical evolution
Meta evolutionary approach
Automated algorithm design
Ensemble differential evolution
Parameter tuning
ISSN:0957-4174, 1873-6793
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Shrnutí:A meta-evolutionary framework called Differential Evolution Ensemble Designer (DEED) has been proposed in this paper to automate the design of DE ensemble algorithms. Given the design components of DE ensembles and a set of optimization problems, DEED evolves effective and robust DE ensemble designs. The design components of DE ensemble algorithms include population management, constituent algorithms in the ensemble, information mixing amongst the sub-populations in the ensemble and the numerical parameters associated with various aspects of the ensemble. DEED employs Dynamic Structured Grammatical Evolution (DSGE) as the meta-evolutionary algorithm. A Backus–Naur form (BNF) grammar has been developed in this paper to represent the design space of DE ensembles and used by DSGE to evolve DE ensemble designs. DEED has been employed to evolve DE ensemble designs for solving 30-dimensional CEC’17 benchmark functions. The evolved designs (both the best design as well as all the final evolved designs) have been validated on CEC’14 and CEC’17 functions at 10, 30 and 50 dimensions and on real-world numerical optimization problems in CEC’11 benchmark suite. The DEED evolved designs have also been tested against the state-of-the-art algorithm configurator - irace. The performance of DEED evolved ensemble designs have been observed to be very competitive against that of manually designed and tuned state-of-the-art DE ensemble algorithms in the literature. DEED has also been demonstrated to evolve both co-operative and competitive style DE ensembles. The simulation experiments demonstrate the effectiveness as well as robustness of the evolved ensemble designs and the reliability of DEED framework in consistently evolving effective DE ensemble designs. •An automated ensemble differential evolution (DE) designer named DEED is presented.•A meta-evolutionary approach to automatically choose ensemble DE design components.•Grammatical evolution forms the meta-evolutionary engine in DEED.•DEED can support researchers and practitioners in the ensemble DE design process.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.121674