Understanding microarray data through applying competent program evolution

Looks, Moshe; Goertzel, Ben; Coelho, Lúcio; Mudado, Mauricio de Alvarenga; Pennachin, Cassio

doi:10.1145/1276958.1277050

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…Estimation Distribution Algorithms (EDA) is another approach to deal with these design or more general structure issues at the probabilistic level [15], as are grammar-based methods [14] and semantic optimization methods [13]. These methods attempt to build probabilistic models, which in turn can be used to generate solutions.…”

Section: Introductionmentioning

confidence: 99%

Second order heuristics in ACGP

Janikow

Aleshunas

Hauschild

2011

Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation

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Genetic Programming explores the problem search space by means of operators and selection. Mutation and crossover operators apply uniformly, while selection is the driving force for the search. Constrained GP changes the uniform exploration to pruned non-uniform, skipping some subspaces and giving preferences to others, according to some heuristics. Adaptable Constrained GP is a methodology for discovery of such useful heuristics. Both methodologies have previously demonstrated their surprising capabilities using only first-order (parent-child) heuristics. Recently, they have been extended to second-order (parent-children) heuristics. This paper describes the second-order processing, and illustrates the usefulness and efficiency of this approach using a simple problem specifically constructed to exhibit strong second-order structure. Categories and Subject Descriptors General TermsDesign, Experimentation. KeywordsGenetic Programming, Heuristics, Search Space. BackgroundGenetic Programming (GP) is an evolutionary computation method bringing together concepts from computer science and nature. It solves a problem at hand by using a population of candidate solutions, represented as chromosomes, and by manipulating the solutions via simulated mutation and crossoverwhile driven by selection to explore better solutions.Even though GP methods have been devised to work with a broad range of possible representations for the candidate solutions, the most common representation is that of a tree [1,6]. These trees are labeled with functions and terminals representing problemspecific elements: functions, connectors, constants, sensors, etc. The actual search space, called genotype space, searched by GP is uniquely determined by the labels, and only constrained by limits on tree size or depth -the trees can be labeled in any arityconsistent manner (the closure property [6]). The corresponding solution space, called phenotype space, depends on the interpretations of the labels -the interpretations provide a mapping from the search space to the solution space or from genotype to phenotype space. Somewhere in the search space, GP attempts to find a point mapped to the actual solution in the solution space, which will provide the solution to the problem at hand. The quality of a single point in the search space is determined by evaluating the mapped solution through a provided black-box fitness function.There are some important issues to consider when designing GP, similar to those of other evolutionary methods yet specific to GP. If a given solution does not have a search space point mapped into it, it will never be discovered. Therefore, the mapping must be onto. To accomplish this, in the absence of detailed information about the problem or solution, the search space needs to be enlarged (a part of the sufficiency principle [6]). This leads to many-to-one mappings, with large redundancy in the representation. To handle these problems, some properties need to be there, among them many-to-one mappings to the better solut...

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Section: Introductionmentioning

confidence: 99%

Second order heuristics in ACGP

Janikow

Aleshunas

Hauschild

2011

Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation

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“…ACGP contrasts from other GP improvement techniques such as Estimation of Distribution Algorithms (EDA) for GP [10], applying grammar-based methodologies to GP [9] and semantic optimization methods [8]. Those methods attempt to build probabilities on labeling specific tree nodes rather than tree-position-independent probabilities as ACGP allows.…”

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confidence: 99%

Cost-benefit analysis of using heuristics in ACGP

Aleshunas¹,

Janikow²

2011

2011 IEEE Congress of Evolutionary Computation (CEC)

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Abstract-Constrained Genetic Programming (CGP) is a method of searching the Genetic Programming search space non-uniformly, giving preferences to certain subspaces according to some heuristics. Adaptable CGP (ACGP) is a method for discovery of the heuristics. CGP and ACGP have previously demonstrated their capabilities using first-order heuristics: parent-child probabilities. Recently, the same advantage has been shown for second-order heuristics: parent-children probabilities. A natural question to ask is whether we can benefit from extending ACGP with deeperorder heuristics. This paper attempts to answer this question by performing cost-benefit analysis while simulating the higher-order heuristics environment. We show that this method cannot be extended beyond the current second or possibly third-order heuristics without a new method to deal with the sheer number of such deeper-order heuristics.Keywords-Genetic Programming, Adaptable Constrained Genetic Programming, Building Block Hypothesis, Heuristic. I. BACKGROUNDGenetic Programming (GP) is an evolutionary computation method merging concepts from computer science and biology. It has been shown to provide robust solutions for problems such as evolving computer programs, designing logic circuits, discovering mathematical equations, and solving other optimization and combinatorial problems where other solutions are not practical or unknown [2].Genetic Programming represents a population of candidate solutions as trees, lists or graphs, but the dominant representation, and the one this paper concentrates on, is the tree representation [2]. These trees are composed of elements from a predetermined set of functions and terminals, called here labels. A given function can label any node whose number of subtrees equals the function arity. Terminal labels, such as constants or variables from the problem environment, label the leaves.The search space is the space of all (up to some size limit) trees that can be labeled with the provided labels. Somewhere in the space, we should find the actual solution to the problem at hand or otherwise GP will not be capable of ever finding the solution. The quality of a single solution-tree, and therefore its "proximity" to the sought solution, is the tree's evaluation through a provided black-box fitness function.After the initial sampling in the GP population, new solutions are evolved using genetic operators such as crossover and mutation, while guided by selection based on fitness evaluations. In crossover, randomly chosen sub-trees are exchanged between two parents. In mutation, a randomly chosen sub-tree is regrown. One critical issue with GP design is the choice of the labels. The labels, along with limitations on explored tree sizes, determine the search space for GP (while population, selection and operators determine the means of searching the space). If the label set is not sufficient, the desired solution does not exist in the search space and therefore GP will never find it. This is often referred to as the sufficiency...

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Understanding microarray data through applying competent program evolution

Cited by 2 publications

References 32 publications

Second order heuristics in ACGP

Second order heuristics in ACGP

Cost-benefit analysis of using heuristics in ACGP

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