Journal ArticleDOI
Survey: A survey of repair methods used as constraint handling techniques in evolutionary algorithms
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This paper provides a survey of the most important repair heuristics used in evolutionary algorithms to solve constrained optimization problems and gives some indications about the design and implementation of hybrid evolutionary algorithms.About:
This article is published in Computer Science Review.The article was published on 2009-08-01. It has received 109 citations till now. The article focuses on the topics: Evolutionary computation & Memetic algorithm.read more
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Journal ArticleDOI
Constraint-Handling in Nature-Inspired Numerical Optimization: Past, Present and Future
TL;DR: An analysis of the most relevant types of constraint-handling techniques that have been adopted with nature-inspired algorithms and the most popular approaches are analyzed in more detail.
Journal ArticleDOI
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.
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Continuous Dynamic Constrained Optimization—The Challenges
Trung Thanh Nguyen,Xin Yao +1 more
TL;DR: This paper presents some investigations on the characteristics that might make DCOPs difficult to solve by some existing dynamic optimization (DO) and constraint handling (CH) algorithms and introduces a set of benchmark problems with these characteristics and test several representative DO and CH strategies.
Journal ArticleDOI
Mean-VaR portfolio optimization: A nonparametric approach
TL;DR: An efficient learning-guided hybrid multi-objective evolutionary algorithm (MODE-GL) is proposed to solve mean-VaR portfolio optimization problems with real-world constraints such as cardinality, quantity, pre-assignment, round-lot and class constraints and outperforms two existing techniques for this important class of portfolio investment problems.
Journal ArticleDOI
A genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem
TL;DR: Comparisons between the proposed genetic programming hyper-heuristic algorithm and the state-of-the-art algorithms indicate the superiority of the proposed GP-HH in computing feasible solutions to the MS-RCPSP.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Journal ArticleDOI
Neural networks and physical systems with emergent collective computational abilities
TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
Book
Genetic Algorithms + Data Structures = Evolution Programs
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.