Sequential Symbolic Regression with Genetic Programming

Oliveira, Luiz Otávio Vilas Bôas; Otero, Fernando E. B.; Pappa, Gisele L.; Albinati, Julio

doi:10.1007/978-3-319-16030-6_5

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…Unlike these methods, Sequential Symbolic Regression [18] (SSR) tackles symbolic regression problems where the target output is not discrete. Like SCGP, SSR spreads the task of approximating the training data across a number of GP runs; each such run is termed an iteration.…”

Section: Introductionmentioning

confidence: 99%

Wave

Medernach

Fitzgerald

Azad

et al. 2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Typically, Genetic Programming (GP) attempts to solve a problem by evolving solutions over a large, and usually predetermined number of generations. However, overwhelming evidence shows that not only does the rate of performance improvement drop considerably after a few early generations, but that further improvement also comes at a considerable cost (bloat). Furthermore, each simulation (a GP run), is typically independent yet homogeneous: it does not re-use solutions from a previous run and retains the same experimental settings.Some recent research on symbolic regression divides work across GP runs where the subsequent runs optimise the residuals from a previous run and thus produce a cumulative solution; however, all such subsequent runs (or iterations) still remain homogeneous thus using a pre-set, large number of generations (50 or more). This work introduces Wave, a divide and conquer approach to GP whereby a sequence of short but sharp, and dependent yet potentially heterogeneous GP runs provides a collective solution; the sequence is akin to a wave such that each member of the sequence (that is, a short GP run) is a period of the wave. Heterogeneity across periods results from varying settings of system parameters, such as population size or number of generations, and also by alternating use of the popular GP technique known as linear scaling.The results show that Wave trains faster and better than both standard GP and multiple linear regression, can prolong discovery through constant restarts (which as a side effect also reduces bloat), can innovatively leverage a learning aid, that is, linear scaling at various stages instead of using it constantly regardless of whether it helps and performs reasonably even with a tiny population size (25) which bodes well for real time or data intensive training.

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Section: Introductionmentioning

confidence: 99%

Wave

Medernach

Fitzgerald

Azad

et al. 2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…Sequential Symbolic Regression [3] (SSR) spreads the task of approximating training data across a number of GP runs, where each such run is termed an iteration. At the end of each iteration, outputs of the original problem are modified based on the use of a geometric semantic crossover [2] on the output of the best evolved solution in the current iteration.…”

Section: Introductionmentioning

confidence: 99%

Wave

Medernach

Fitzgerald

Azad

et al. 2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

This work introduces Wave, a divide and conquer approach to GP whereby a sequence of short, and dependent but potentially heterogeneous GP runs provides a collective solution; the sequence akins a wave such that each short GP run is a period of the wave. Heterogeneity across periods results from varying settings of system parameters, such as population size or number of generations, and also by alternating use of the popular GP technique known as linear scaling.

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“…Many di erent approaches have been proposed in the last 25 years (see Sec. 5.2 of [12] for an overview). However, none of them has shown clear superiority in systematically identifying salient BBs [4].…”

Section: Introductionmentioning

confidence: 99%

Scalable genetic programming by gene-pool optimal mixing and input-space entropy-based building-block learning

Virgolin

Alderliesten

Witteveen

et al. 2017

Proceedings of the Genetic and Evolutionary Computation Conference

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e Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a recently introduced model-based EA that has been shown to be capable of outperforming state-of-the-art alternative EAs in terms of scalability when solving discrete optimization problems. One of the key aspects of GOMEA's success is a variation operator that is designed to extensively exploit linkage models by e ectively combining partial solutions. Here, we bring the strengths of GOMEA to Genetic Programming (GP), introducing GP-GOMEA. Under the hypothesis of having li le problem-speci c knowledge, and in an e ort to design easy-to-use EAs, GP-GOMEA requires no parameter speci cation. On a set of well-known benchmark problems we nd that GP-GOMEA outperforms standard GP while being on par with more recently introduced, state-of-the-art EAs. We furthermore introduce Input-space Entropy-based Building-block Learning (IEBL), a novel approach to identifying and encapsulating relevant building blocks (subroutines) into new terminals and functions. On problems with an inherent degree of modularity, IEBL can contribute to compact solution representations, providing a large potential for knock-on e ects in performance. On the di cult, but highly modular Even Parity problem, GP-GOMEA+IEBL obtains excellent scalability, solving the 14-bit instance in less than 1 hour.

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Sequential Symbolic Regression with Genetic Programming

Cited by 10 publications

References 18 publications

Wave

Wave

Wave

Scalable genetic programming by gene-pool optimal mixing and input-space entropy-based building-block learning

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