Journal ArticleDOI
Surrogate-assisted evolutionary computation: Recent advances and future challenges
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This paper provides a concise overview of the history and recent developments in surrogate-assisted evolutionary computation and suggests a few future trends in this research area.Abstract:
Surrogate-assisted, or meta-model based evolutionary computation uses efficient computational models, often known as surrogates or meta-models, for approximating the fitness function in evolutionary algorithms. Research on surrogate-assisted evolutionary computation began over a decade ago and has received considerably increasing interest in recent years. Very interestingly, surrogate-assisted evolutionary computation has found successful applications not only in solving computationally expensive single- or multi-objective optimization problems, but also in addressing dynamic optimization problems, constrained optimization problems and multi-modal optimization problems. This paper provides a concise overview of the history and recent developments in surrogate-assisted evolutionary computation and suggests a few future trends in this research area.read more
Citations
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Recent advances in differential evolution – An updated survey
TL;DR: It is found that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research on DE.
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Robots that can adapt like animals
Antoine Cully,Jeff Clune,Danesh Tarapore,Danesh Tarapore,Danesh Tarapore,Jean-Baptiste Mouret +5 more
TL;DR: An intelligent trial-and-error algorithm is introduced that allows robots to adapt to damage in less than two minutes in large search spaces without requiring self-diagnosis or pre-specified contingency plans, and may shed light on the principles that animals use to adaptation to injury.
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Metaheuristic research: a comprehensive survey
TL;DR: A survey of metaheuristic research in literature consisting of 1222 publications from year 1983 to 2016 is performed to highlight potential open questions and critical issues raised in literature and provides guidance for future research to be conducted more meaningfully.
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Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]
TL;DR: This paper reviews traditional as well as state-of-the-art ensemble methods and thus can serve as an extensive summary for practitioners and beginners.
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Bio-inspired computation: Where we stand and what's next
Javier Del Ser,Javier Del Ser,Eneko Osaba,Daniel Molina,Xin-She Yang,Sancho Salcedo-Sanz,David Camacho,Swagatam Das,Ponnuthurai Nagaratnam Suganthan,Carlos A. Coello Coello,Francisco Herrera +10 more
TL;DR: The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques.
References
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The design and analysis of computer experiments
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Journal ArticleDOI
Design and analysis of computer experiments
Sonja Kuhnt,David M. Steinberg +1 more
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Book
Engineering Design via Surrogate Modelling: A Practical Guide
TL;DR: This chapter discusses the design and exploration of a Surrogate-based kriging model, and some of the techniques used in that process, as well as some new approaches to designing models based on the data presented.
Journal ArticleDOI
Evolutionary optimization in uncertain environments-a survey
Yaochu Jin,Jürgen Branke +1 more
TL;DR: This paper attempts to provide a comprehensive overview of the related work within a unified framework on addressing different uncertainties in evolutionary computation, which has been scattered in a variety of research areas.
Journal ArticleDOI
Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation
TL;DR: This paper features a survey of about 250 IEC research papers and discusses the IEC from the point of the future research direction of computational intelligence.