Learning with genetic algorithms: an overview
Machine Language
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Approximation algorithms for bin packing: a survey
Approximation algorithms for NP-hard problems
BISON: a fast hybrid procedure for exactly solving the one-dimensional bin packing problem
Computers and Operations Research
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Heuristic Solution of Open Bin Packing Problems
Journal of Heuristics
Classifier Systems and the Animat Problem
Machine Learning
A Critical Review of Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
Hyper-heuristics: Learning To Combine Simple Heuristics In Bin-packing Problems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Intelligent behavior as an adaptation to the task environment
Intelligent behavior as an adaptation to the task environment
A new representation and operators for genetic algorithms applied to grouping problems
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Fleet estimation for defence logistics using a multi-objective learning classifier system
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A new hyper-heuristic as a general problem solver: an implementation in HyFlex
Journal of Scheduling
A new methodology for the automatic creation of adaptive hybrid algorithms
Intelligent Data Analysis
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Evolutionary Algorithms (EAs) have been successfully reported by academics in a wide variety of commercial areas. However, from a commercial point of view, the story appears somewhat different; the number of success stories does not appear to be as significant as those reported by academics. For instance, Heuristic Algorithms (HA) are still very widely used to tackle practical problems in operations research, where many of these are NP-hard and exhaustive search is often computationally intractable. There are a number of logical reasons why practitioners do not embark so easily in the development and use of EAs. This work is concerned with a new line of research based on bringing together these two approaches in a harmonious way. The idea is that instead of using an EA to learn the solution of a specific problem, use it to find an algorithm, i.e. a solution process that can solve well a large family of problems by making use of familiar heuristics. The work of the authors is novel in two ways: within the Learning Classifier Systems (LCS) current body of research, it represents the first attempt to tackle the Bin Packing problem (BP), a different kind of problem to those already studied by the LCS community, and from the Hyper-Heuristics (HH) framework, it represents the first use of LCS as the learning paradigm. Several reward schema based on single or multiple step environments are studied in this paper, tested on a very large set of BP problems and a small set of widely used HAs. Results of the approach are encouraging, showing outperformance over all HAs used individually and over previously reported work by the authors, including non-LCS (a GA based approach used for the same BP set of problems) and LCS (using single step environments). Several findings and future lines of work are also outlined.