A Competitive Building Block Hypothesis

Ryan, Conor; Majeed, Hammad; Azad, R. Muhammad Atif

doi:10.1007/978-3-540-24855-2_73

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…These have themselves spawned several ways to describe building blocks: a subtree to a solution tree [1], a rooted subtree [22], a block of code [13]. Recent work has used both general subtrees [16], [24] and rooted subtrees/structures as models for building blocks [5], [23]. With no clear consensus of the better between the two, we believe that it is dangerous to simply ignore unrooted subtrees/structures as possible building blocks in a GP system.…”

Section: Building Block Analysismentioning

confidence: 99%

Effects of program simplification on simple building blocks in Genetic Programming

Wong

Zhang

2007

2007 IEEE Congress on Evolutionary Computation

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This paper investigates the effects on building blocks of using simplification in a Genetic Programming (GP) system to combat the problem of code bloat. The evolved genetic programs are simplified online during the evolutionary process using algebraic simplification rules and hashing techniques. A simplified form of building block (numerical-nodes) is tracked throughout individual GP runs both when using or not using online simplification of evolved genetic programs. The results suggest that online simplification disrupts existing potential building blocks during the evolution process. However, GP with simplification is capable of creating new building blocks which are used to form a more accurate solution, when compared to the standard GP. The effectiveness of GP systems utilising simplification can be correlated to the creation of these new building blocks.

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Section: Building Block Analysismentioning

confidence: 99%

Effects of program simplification on simple building blocks in Genetic Programming

Wong

Zhang

2007

2007 IEEE Congress on Evolutionary Computation

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“…[49][50][51][52]). The major concerns related to the applicability of the BBH in GP are similar to ones for GAs along with the following.…”

Section: Building Block Hypothesismentioning

confidence: 99%

“…is proposed by Ryan et al in [51]. According to this there are three areas of discovery in GP trees: (a) the common area, i.e.…”

Section: Building Block Hypothesismentioning

confidence: 99%

Towards identifying salient patterns in genetic programming individuals

Joó

Neirotti

2009

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation

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This thesis addresses the problem of offline identification of salient patterns in genetic programming individuals. It discusses the main issues related to automatic pattern identification systems, namely that these (a) should help in understanding the final solutions of the evolutionary run, (b) should give insight into the course of evolution and (c) should be helpful in optimizing future runs. Moreover, it proposes an algorithm, Extended Pattern Growing Algorithm ([E]PGA) to extract, filter and sort the identified patterns so that these fulfill as many as possible of the following criteria: (a) they are representative for the evolutionary run and/or search space, (b) they are human-friendly and (c) their numbers are within reasonable limits. The results are demonstrated on six problems from different domains.

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“…Concentrating solely on rooted-subtrees also reduces the complexity of schema theories tremendously [71,78]. However, recent work has used both general subtrees [52,81] and rooted subtrees/structures as models for building blocks [80,79], giving no clear consensus on which is more "useful" to analyse. Daida et al [14] looks at the simplified analysis of single-node subtrees in order to help determine what a building block is.…”

Section: Building Block Hypothesis and Schema Theory In Gpmentioning

confidence: 99%

Removing Redundancy and Reducing Fitness Evaluation Costs in Genetic Programming

Wong¹

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<p>One of the greater issues in Genetic Programming (GP) is the computational effort required to run the evolution and discover a good solution. Phenomena such as program bloating (where genetic programs rapidly grow in size) can quickly exhaust available memory resources and slow down the evolutionary process, while the heavy cost of performing fitness evaluation can make problems which have a lot of available data very slow to solve. These issues may limit GP in some tasks it can appropriately be applied to, as well as inhibit its applications in time/space sensitive environments. In this thesis, we look at developing solutions to some of these issues in GP computational cost. First, we develop an algebraic program simplification method based on simple rules and hashing techniques, and use this method in conjunction with the standard GP on a variety of tasks. Our results suggest that program simplification can lead to a significant reduction in program size, while not significantly changing the effectiveness of the systems in finding solution programs. Secondly, we analyse the effects of program simplification on the internal GP "building blocks" to investigate whether simplification is a destructive or constructive force. Using two models for building blocks (numerical-nodes and the more complex fixed-depth subtree), we track building blocks through GP runs on a symbolic regression problem, both with and without using simplification. We find that the program simplification process can both disrupt and construct building blocks in the GP populations. However, GP systems using simplification appear to retain important building blocks, and the simplification process appears to lead to an increase in genetic diversity. These may help explain why using simplification does not reduce the effectiveness of GP systems in solving tasks. Lastly, we develop two methods of reducing the cost of fitness evaluation by reducing the number of node evaluations performed. The first method is elitism avoidance, which avoids re-evaluating programs which have been placed in the population using elitismreproduction. Thismethod reduces the CPU time for evolving solutions for six different GP tasks. The second method is a subtree caching mechanism which store fitness evaluations for subtrees in a cache so that they may be fetched when these subtrees are encountered in future fitness evaluations. Results suggest that using this mechanism can significantly reduce both the number of node evaluations and the overall CPU time used in evolving solutions, without reducing the fitness of the solutions produced.</p>

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A Competitive Building Block Hypothesis

Cited by 9 publications

References 11 publications

Effects of program simplification on simple building blocks in Genetic Programming

Effects of program simplification on simple building blocks in Genetic Programming

Towards identifying salient patterns in genetic programming individuals

Removing Redundancy and Reducing Fitness Evaluation Costs in Genetic Programming

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