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A Classification of Hyperheuristic Approaches
"... The current state of the art in hyperheuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In ..."
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The current state of the art in hyperheuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present and overview of previous categorisations of hyperheuristics and provide a unified classification and definition which captures the work that is being undertaken in this field. We distinguish between two main hyperheuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goal is to both clarify the main features of existing techniques and to suggest new directions for hyperheuristic research.
Exploring Hyperheuristic Methodologies with Genetic Programming
"... Hyperheuristics represent a novel search methodology that is motivated by the goal of automating the process of selecting or combining simpler heuristics in order to solve hard computational search problems. An extension of the original hyperheuristic idea is to generate new heuristics which are n ..."
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Cited by 34 (14 self)
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Hyperheuristics represent a novel search methodology that is motivated by the goal of automating the process of selecting or combining simpler heuristics in order to solve hard computational search problems. An extension of the original hyperheuristic idea is to generate new heuristics which are not currently known. These approaches operate on a search space of heuristics rather than directly on a search space of solutions to the underlying problem which is the case with most metaheuristics implementations. In the majority of hyperheuristic studies so far, a framework is provided with a set of human designed heuristics, taken from the literature, and with good measures of performance in practice. A less well studied approach aims to generate new heuristics from a set of potential heuristic components. The purpose of this chapter is to discuss this class of hyperheuristics, in which Genetic Programming is the most widely used methodology. A detailed discussion is presented including the steps needed to apply this technique, some representative case studies, a literature review of related work, and a discussion of relevant issues. Our aim is to convey the exciting potential of this innovative approach for automating the heuristic design process
A Genetic Programming HyperHeuristic Approach for Evolving Two Dimensional Strip Packing Heuristics
"... We present a genetic programming system to evolve reusable heuristics for the two dimensional strip packing problem. The evolved heuristics are constructive, and decide both which piece to pack next and where to place that piece, given the current partial solution. This work contributes to a growing ..."
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We present a genetic programming system to evolve reusable heuristics for the two dimensional strip packing problem. The evolved heuristics are constructive, and decide both which piece to pack next and where to place that piece, given the current partial solution. This work contributes to a growing research area which represents a paradigm shift in search methodologies. Instead of using evolutionary computation to search a space of solutions, we employ it to search a space of heuristics for the problem. One of the motivations for this research area is that once a heuristic has been evolved, it can be reused on any new problem instance, meaning that the time consuming evolutionary process need only be run once to obtain a solution to many problem instances. A second motivation is to research methods to automate the heuristic design process. It has been stated in the literature that humans are very good at identifying good building blocks for solution methods, however the task of intelligently searching through all of the potential combinations of these components may be better suited to a computer. With such tools at their disposal, heuristic designers are then free to commit more of their time to the creative process of determining good components, while the computer takes on some of the design process by intelligently combining these components. The contribution of this paper is to show that a genetic programming hyperheuristic can be employed to automatically generate heuristics which are often better than the humandesigned state of the art constructive heuristics, in a very well studied area.
The Scalability of Evolved On Line Bin Packing Heuristics
"... The on line bin packing problem concerns the packing of pieces into the least number of bins possible, as the pieces arrive in a sequential fashion. In previous work, we used genetic programming to evolve heuristics for this problem, which beat the human designed ‘best fit’ algorithm. Here we exami ..."
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The on line bin packing problem concerns the packing of pieces into the least number of bins possible, as the pieces arrive in a sequential fashion. In previous work, we used genetic programming to evolve heuristics for this problem, which beat the human designed ‘best fit’ algorithm. Here we examine the performance of the evolved heuristics on larger instances of the problem, which contain many more pieces than the problem instances used in training. In previous work, we concluded that we could confidently apply our heuristics to new instances of the same class of problem. Now we can make the additional claim that we can confidently apply our heuristics to problems of much larger size, not only without deterioration of solution quality, but also within a constant factor of the performance obtained by ‘best fit’. Interestingly, our evolved heuristics respond to the number of pieces in a problem instance although they have no explicit access to that information. We also comment on the important point that, when solutions are explicitly constructed for single problem instances, the size of the search space explodes. However, when working in the space of algorithmic heuristics, the distribution of functions represented in the search space reaches some limiting distribution and therefore the combinatorial explosion can be controlled.
Tuning & Simplifying Heuristical Optimization
, 2010
"... This thesis is about the tuning and simplification of blackbox (directsearch, derivativefree) optimization methods, which by definition do not use gradient information to guide their search for an optimum but merely need a fitness (cost, error, objective) measure for each candidate solution to th ..."
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This thesis is about the tuning and simplification of blackbox (directsearch, derivativefree) optimization methods, which by definition do not use gradient information to guide their search for an optimum but merely need a fitness (cost, error, objective) measure for each candidate solution to the optimization problem. Such optimization methods often have parameters that influence their behaviour and efficacy. A MetaOptimization technique is presented here for tuning the behavioural parameters of an optimization method by employing an additional layer of optimization. This is used in a number of experiments on two popular optimization methods, Differential Evolution and Particle Swarm Optimization, and unveils the true performance capabilities of an optimizer in different usage scenarios. It is found that stateoftheart optimizer variants with their supposedly adaptive behavioural parameters do not have a general and consistent performance advantage but are outperformed in several cases by simplified optimizers, if only the behavioural parameters are tuned properly.
Evolving Reusable 3D Packing Heuristics with Genetic Programming
"... This paper compares the quality of reusable heuristics designed by genetic programming (GP) to those designed by human programmers. The heuristics are designed for the three dimensional knapsack packing problem. Evolutionary computation has been employed many times to search for good quality solutio ..."
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This paper compares the quality of reusable heuristics designed by genetic programming (GP) to those designed by human programmers. The heuristics are designed for the three dimensional knapsack packing problem. Evolutionary computation has been employed many times to search for good quality solutions to such problems. However, actually designing heuristics with GP for this problem domain has never been investigated before. In contrast, the literature shows that it has taken years of experience by human analysts to design the very effective heuristic methods that currently exist. Hyperheuristics search a space of heuristics, rather than directly searching a solution space. GP operates as a hyperheuristic in this paper, because it searches the space of heuristics that can be constructed from a given set of components. We show that GP can design simple, yet effective, standalone constructive heuristics. While these heuristics do not represent the best in the literature, the fact that they are designed by evolutionary computation, and are human competitive, provides evidence that further improvements in this GP methodology could yield heuristics superior to those designed by humans.
Policy Matrix Evolution for Generation of Heuristics
"... Online binpacking is a wellknown problem in which immediate decisions must be made about the placement of items with various sizes into fixed capacity bins. The associated decisions can be based on an index policy in which each decision option is independently given a value and the highest value c ..."
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Online binpacking is a wellknown problem in which immediate decisions must be made about the placement of items with various sizes into fixed capacity bins. The associated decisions can be based on an index policy in which each decision option is independently given a value and the highest value choice is selected. In this paper, we represent such heuristics for online bin packing as a simple matrix of scores. We then use a genetic algorithm to search for matrices giving good performance. This might be regarded as parameter tuning of the packing heuristic but in which a finegrained representation is used and so the number of parameters is much larger than in standard parameter tuning. The evolved matrices perform better than the standard heuristics. They also reveal interesting structures and so have impact on questions of how heuristic score functions should be represented and what structure they might be expected to exhibit.
Evolution of hyperheuristics for the biobjective 0/1 knapsack problem by multiobjective genetic programming
 IN: GECCO 2008, ACM
, 2008
"... The 0/1 knapsack problem is one of the most exhaustively studied NPhard combinatorial optimization problems. Many different approaches have been taken to obtain an approximate solution to the problem in polynomial time. Here we consider the biobjective 0/1 knapsack problem. The contribution of th ..."
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Cited by 6 (0 self)
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The 0/1 knapsack problem is one of the most exhaustively studied NPhard combinatorial optimization problems. Many different approaches have been taken to obtain an approximate solution to the problem in polynomial time. Here we consider the biobjective 0/1 knapsack problem. The contribution of this paper is to show that a genetic programming system can evolve a set of heuristics that can give solutions on the Pareto front for multiobjective combinatorial problems. The genetic programming (GP) system outlined here evolves a heuristic which decides whether or not to add an item to the knapsack in such a way that the final solution is one of the Pareto optimal solutions. Moreover, the Pareto front obtained from the GP system is comparable to the front obtained from other humandesigned heuristics. We discuss the issue of the diversity of the obtained Pareto front and the application of stronglytyped GP as a means of obtaining better diversity.
M (2009) There is a free lunch for hyperheuristics, genetic programming and computer scientists. In: Vanneschi L et al (eds
 EuroGP’09: Proceedings of the 12th
"... Abstract. In this paper we prove that in some practical situations, there is a free lunch for hyperheuristics, i.e., for search algorithms that search the space of solvers, searchers, metaheuristics and heuristics for problems. This has consequences for the use of genetic programming as a method t ..."
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Abstract. In this paper we prove that in some practical situations, there is a free lunch for hyperheuristics, i.e., for search algorithms that search the space of solvers, searchers, metaheuristics and heuristics for problems. This has consequences for the use of genetic programming as a method to discover new search algorithms and, more generally, problem solvers. Furthermore, it has also rather important philosophical consequences in relation to the efforts of computer scientists to discover useful novel search algorithms. 1
Memetic algorithms
 In: Metaheuristics in Neural Networks Learning
, 2006
"... Abstract Memetic Algorithms have become one of the key methodologies behind solvers that are capable of tackling very large, realworld, optimisation problems. They are being actively investigated in research institutions as well as broadly applied in industry. In this chapter we provide a pragmatic ..."
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Abstract Memetic Algorithms have become one of the key methodologies behind solvers that are capable of tackling very large, realworld, optimisation problems. They are being actively investigated in research institutions as well as broadly applied in industry. In this chapter we provide a pragmatic guide on the key design issues underpinning Memetic Algorithms (MA) engineering. We begin with a brief contextual introduction to Memetic Algorithms and then move on to define a Pattern Language for MAs. For each pattern, an associated design issue is tackled and illustrated with examples from the literature. In the last section of this chapter we “fast forward ” to the future and mention what, in our mind, are the key challenges that scientistis and practitioner will need to face if Memetic Algorithms are to remain a relevant technology in the next 20 years. 1