Grammar-Guided Genetic Programming

Grammar-Guided Genetic Programming

Daniel Manrique (Inteligencia Artificial, Facultad de Informatica, UPM, Spain), Juan Ríos (Inteligencia Artificial, Facultad de Informatica, UPM, Spain) and Alfonso Rodríguez-Patón (Inteligencia Artificial, Facultad de Informatica, UPM, Spain)
Copyright: © 2009 |Pages: 7
DOI: 10.4018/978-1-59904-849-9.ch114
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Abstract

Evolutionary computation (EC) is the study of computational systems that borrow ideas from and are inspired by natural evolution and adaptation (Yao & Xu, 2006, pp. 1-18). EC covers a number of techniques based on evolutionary processes and natural selection: evolutionary strategies, genetic algorithms and genetic programming (Keedwell & Narayanan, 2005). Evolutionary strategies are an approach for efficiently solving certain continuous problems, yielding good results for some parametric problems in real domains. Compared with genetic algorithms, evolutionary strategies run more exploratory searches and are a good option when applied to relatively unknown parametric problems. Genetic algorithms emulate the evolutionary process that takes place in nature. Individuals compete for survival by adapting as best they can to the environmental conditions. Crossovers between individuals, mutations and deaths are all part of this process of adaptation. By substituting the natural environment for the problem to be solved, we get a computationally cheap method that is capable of dealing with any problem, provided we know how to determine individuals’ fitness (Manrique, 2001). Genetic programming is an extension of genetic algorithms (Couchet, Manrique, Ríos & Rodríguez- Patón, 2006). Its aim is to build computer programs that are not expressly designed and programmed by a human being. It can be said to be an optimization technique whose search space is composed of all possible computer programs for solving a particular problem. Genetic programming’s key advantage over genetic algorithms is that it can handle individuals (computer programs) of different lengths. Grammar-guided genetic programming (GGGP) is an extension of traditional GP systems (Whigham, 1995, pp. 33-41). The difference lies in the fact that they employ context-free grammars (CFG) that generate all the possible solutions to a given problem as sentences, establishing this way the formal definition of the syntactic problem constraints, and use the derivation trees for each sentence to encode these solutions (Dounias, Tsakonas, Jantzen, Axer, Bjerregard & von Keyserlingk, D. 2002, pp. 494-500). The use of this type of syntactic formalisms helps to solve the so-called closure problem (Whigham, 1996). To achieve closure valid individuals (points that belong to the search space) should always be generated. As the generation of invalid individuals slows down convergence speed a great deal, solving this problem will very much improve the GP search capability. The basic operator directly affecting the closure problem is crossover: crossing two (or any) valid individuals should generate a valid offspring. Similarly, this is the operator that has the biggest impact on the process of convergence towards the optimum solution. Therefore, this article reviews the most important crossover operators employed in GP and GGGP, highlighting the weaknesses existing nowadays in this area of research. We also propose a GGGP system. This system incorporates the original idea of employing ambiguous CFG to overcome these weaknesses, thereby increasing convergence speed and reducing the likelihood of trapping in local optima. Comparative results are shown to empirically corroborate our claims.
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Background

Koza defined one of the first major crossover operators (KX) (1992). This approach randomly swaps subtrees in both parents to generate offspring. Therefore, it tends to disaggregate the so-called building blocks across the trees (that represent the individuals). The building blocks are those subtrees that improve fitness. This over-expansion has a negative effect on the fitness of the individuals. Also, this operator’s excessive exploration capability leads to another weakness: an increase in the size of individuals, which affects system performance, and results in a lower convergence speed (Terrio & Heywood, 2002). This effect is known as bloat or code bloat.

Key Terms in this Chapter

Ambiguous Grammar: Any grammar in which different derivation trees can generate the same sentence.

Closure Problem: Phenomenon that involves always generating syntactically valid individuals.

Convergence: Process by means of which an algorithm (in this case an evolutionary system) gradually approaches a solution. A genetic programming system is said to have converged when most of the individuals in the population are equal or when the system cannot evolve any further.

Grammar-Guided Genetic Programming: The application of analytical methods and tools to data for the purpose of identifying patterns, relationships or obtaining systems that perform useful tasks such as classification, prediction, estimation, or affinity grouping.

Intron: Segment of code within an individual (subtree) that does not modify the fitness, but is on the side of convergence process.

Code Bloat: Phenomenon to be avoided in a genetic programming system convergence process involving the uncontrolled growth, in terms of size and complexity, of individuals in the population

Fitness: Measure associated with individuals in an evolutionary algorithm population to determine how good the solution they represent is for the problem.

Genetic Programming: A variant of genetic algorithms that uses simulated evolution to discover functional programs to solve a task.

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