Penalty Function Methods for Constrained Optimization with Genetic Algorithms
TLDR
These penalty-based methods for handling constraints in Genetic Algorithms are presented and discussed and their strengths and weaknesses are discussed.Abstract:
Genetic Algorithms are most directly suited to unconstrained optimization. Application of Genetic Algorithms to constrained optimization problems is often a challenging effort. Several methods have been proposed for handling constraints. The most common method in Genetic Algorithms to handle constraints is to use penalty functions. In this paper, we present these penalty-based methods and discuss their strengths and weaknesses.read more
Citations
More filters
Proceedings ArticleDOI
Task Offloading for Post-disaster Rescue in Vehicular Fog Computing-assisted UAV Networks
TL;DR: In this article , a joint UAV and vehicular task assignment scheme was proposed with the aim of optimizing the performance of the network, where a GA-IWO algorithm was used to achieve the approximately optimal task assignment strategy.
Proceedings ArticleDOI
Optimization of control system for Nitrifying Process in two nitro chlorobenzene production
TL;DR: An intelligent optimization control system (IOCS) to implement the modeling, optimization, and control of the NP by improved back-propagation neural networks, c-means clustering, genetic and chaos approaches is presented.
Journal ArticleDOI
A comparison of first-come-first-served and multidimensional heuristic approaches for asset allocation of floor cleaning machines
Journal ArticleDOI
Using a Variational Autoencoder to Learn Valid Search Spaces of Safely Monitored Autonomous Robots for Last-Mile Delivery
TL;DR: In this paper , a hybrid machine-learning optimization approach called constrained optimization in learned latent space (COIL) is used to optimize autonomous robot timings to maximize deliveries, while ensuring that there are never too many robots running simultaneously so that they can be monitored safely.
Proceedings ArticleDOI
Intelligent Control System for Cleaning Process in Sugar Refinery
TL;DR: An intelligent optimization control system (IOCS) to implement the modeling, optimization, and control of the CP by improved back-propagation neural networks, c-means clustering, genetic and chaos approaches is presented.
References
More filters
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.
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.
Book
Nonlinear Programming: Theory and Algorithms
TL;DR: The book is a solid reference for professionals as well as a useful text for students in the fields of operations research, management science, industrial engineering, applied mathematics, and also in engineering disciplines that deal with analytical optimization techniques.
Journal ArticleDOI
An efficient constraint handling method for genetic algorithms
TL;DR: GA's population-based approach and ability to make pair-wise comparison in tournament selection operator are exploited to devise a penalty function approach that does not require any penalty parameter to guide the search towards the constrained optimum.
Book
Evolutionary algorithms in theory and practice
TL;DR: In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming within a unified framework, thereby clarifying the similarities and differences of these methods.