Evolutionary Design of FreeCell Solvers

Elyasaf, Achiya; Hauptman, Ami; Sipper, Moshe

doi:10.1109/tciaig.2012.2210423

Cited by 24 publications

(17 citation statements)

References 23 publications

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“…A recent line of research has attempted to build a more general form of evolutionbased game intelligence by employing a structure known as a policy, which is an ordered set of search-guiding rules (Hauptman et al 2009;Elyasaf et al 2012). Policies are complex structures that allow one to define specific conditions under which certain actions are performed.…”

Section: Gamesmentioning

confidence: 99%

Genetic Programming Theory and Practice XII

Riolo¹,

Worzel²,

Kotanchek³

2015

Genetic and Evolutionary Computation

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Section: Gamesmentioning

confidence: 99%

Genetic Programming Theory and Practice XII

Riolo¹,

Worzel²,

Kotanchek³

2015

Genetic and Evolutionary Computation

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“…However, there are methodologies that can cut across categories. For example, we can see hybrid methodologies that combine constructive with perturbation heuristics [51], or heuristic selection with heuristic generation [42,60,66,71].…”

Section: Hyper-heuristicsmentioning

confidence: 99%

“…In [42], for example, the available instances are divided into groups according to their level of difficulty. Initially, solutions are evaluated using easy instances and, as the solutions get better, the initial instances are replaced by harder ones.…”

Section: Challenges Relating To Datasets and Trainingmentioning

confidence: 99%

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

Pappa

Ochoa

Hyde

et al. 2013

Genet Program Evolvable Mach

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The fields of machine meta-learning and hyper-heuristic optimisation have developed mostly independently of each other, although evolutionary algorithms (particularly genetic programming) have recently played an important role in the development of both fields. Recent work in both fields shares a common goal, that of automating as much of the algorithm design process as possible. In this paper we first provide a historical perspective on automated algorithm design, and then we discuss similarities and differences between meta-learning in the field of supervised machine learning (classification) and hyper-heuristics in the field of optimisation. This discussion focuses on the dimensions of the problem space, the algorithm space and the performance measure, as well as clarifying important issues related to different levels of automation and generality in both fields. We also discuss important research directions, challenges and foundational issues in meta-learning and hyper-heuristic research. It is important to emphasize that this paper is not a survey, as several surveys on the areas of meta-learning and hyper-heuristics (separately) have been previously published. The main contribution of the paper is to contrast meta-learning and hyper-heuristics methods and concepts, in order to 123Genet Program Evolvable Mach DOI 10.1007/s10710-013-9186-9 promote awareness and cross-fertilisation of ideas across the (by and large, nonoverlapping) different communities of meta-learning and hyper-heuristic researchers. We hope that this cross-fertilisation of ideas can inspire interesting new research in both fields and in the new emerging research area which consists of integrating those fields.

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“…can be well-suited to searching through very large, high-dimensional landscapes of alternatives [36], even when finding optima for imprecise or noisy signals. Such search techniques have been able to rapidly solve a number of interesting and difficult problems (e.g., evolutionary algorithms have produced human competitive results [37] in music synthesis [38], electron microscopy [39] and game solvers [40]). …”

Section: Ix-xiii 4 13 19mentioning

confidence: 99%

“…The average number of genetic operations per fitness evaluation is 2.66, and the average number of operations to produce a successful repair is 4.22. Summing over all individuals in the population (40) and considering that a repair is discovered on average within 3.6 generations on this dataset, the search on average requires 667 genetic operations to discover the initial repair on these benchmarks. The contribution of the individual operations is very noisy, and it is difficult to draw significant conclusions based on this data.…”

Section: Operatorsmentioning

confidence: 99%

Automatic Program Repair Using Genetic Programming

Goues¹

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Software quality is an urgent problem. There are so many bugs in industrial program source code that mature software projects are known to ship with both known and unknown bugs [1], and the number of outstanding defects typically exceeds the resources available to address them [2]. This has become a pressing economic problem whose costs in the United States can be measured in the billions of dollars annually [3].A dominant reason that software defects are so expensive is that fixing them remains a manual process. The process of identifying, triaging, reproducing, and localizing a particular bug, coupled with the task of understanding the underlying error, identifying a set of code changes that address it correctly, and then verifying those changes, costs both time [4] and money, and the cost of repairing a defect can increase by orders of magnitude as development progresses [5]. As a result, many defects, including critical security defects [6], remain unaddressed for long periods of time [7]. Moreover, humans are error-prone, and many human fixes are imperfect, in that they are either incorrect or lead to crashes, hangs, corruption, or security problems [8]. As a result, defect repair has become a major component of software maintenance, which in turn consumes up to 90% of the total lifecycle cost of a given piece of software [9].Although considerable research attention has been paid to supporting various aspects of the manual debugging process [10,11], and also to preempting or dynamically addressing particular classes of vulnerabilities, such as buffer overruns [12,13], there exist virtually no previous automated solutions that address the synthesis of patches for general bugs as they are reported in real-world software.The primary contribution of this dissertation is GenProg, one of the very first automatic solutions designed to help alleviate the manual bug repair burden by automatically and generically patching bugs in deployed and legacy software. GenProg uses a novel genetic programming algorithm, guided by test cases and domain-specific operators, to affect scalable, expressive, and high quality automated repair. We present experimental evidence to substantiate our claims that GenProg can repair multiple types of bugs in multiple types of programs, and that it can repair a large proportion of the bugs that human developers address in practice (that it is expressive); that it scales to real-world system sizes (that it is scalable); and that it produces repairs that are of sufficiently high quality. Over the course of this evaluation, we contribute new benchmark sets of real bugs in real open-source software and novel experimental frameworks for quantitatively evaluating an automated repair technique. We also contribute a novel characterization of i Chapter 0Abstract ii the automated repair search space, and provide analysis both of that space and of the performance and scaling behavior of our technique.General automated software repair was unheard of in 2009. In 2013, it has its own multi-paper s...

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Evolutionary Design of FreeCell Solvers

Cited by 24 publications

References 23 publications

Genetic Programming Theory and Practice XII

Genetic Programming Theory and Practice XII

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

Automatic Program Repair Using Genetic Programming

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