NEAT, There’s No Bloat

Trujillo, Leonardo; Muñoz, Luis; Naredo, Enrique; Martínez, Yuliana

doi:10.1007/978-3-662-44303-3_15

Cited by 6 publications

(10 citation statements)

References 18 publications

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“…The results show that a neat-GP based search can outperform a standard GP search, based on test performance and especially with regards to solution size and depth. These results agree with those reported in [29], with the added advantage that the bloat control method does not incur in any additional computational cost exhibited by other state-of-the-art bloat control methods [23,26].…”

Section: Introductionsupporting

confidence: 91%

“…From this general perspective, we state that NEAT is a special form of GP, where evolved solutions express instances from a narrow class of functions or programs, those that can be represented as NNs. However, the results obtained with NEAT can provide useful insights to the GP paradigm as a whole [29].…”

Section: The Main Components In Neatmentioning

confidence: 99%

“…The contribution of our work is the development of a GP-based system based on the NeuroEvolution of Augmenting Topologies (NEAT) algorithm, which uses speciation to protect novel solution topologies and promote the incremental evolution of complexity [28]. In our recent work, we showed that NEAT can run bloat free, using a careful parametrization and system configuration [29]. However, it was unclear if the results obtained from neuroevolution could be replicated in a traditional GP domain.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, she proposed the Flat Operator Equalization bloat control method (Flat-OE), which explicitly forces the evolving population to follow a uniform distribution of program sizes, while the range of the distribution remains constant across all generations.The contribution of our work is the development of a GP-based system based on the NeuroEvolution of Augmenting Topologies (NEAT) algorithm, which uses speciation to protect novel solution topologies and promote the incremental evolution of complexity [28]. In our recent work, we showed that NEAT can run bloat free, using a careful parametrization and system configuration [29]. However, it was unclear if the results obtained from neuroevolution could be replicated in a traditional GP domain.The proposed algorithm is called neat-GP and it can be understood as a stripped down version of the original NEAT algorithm, which is adapted to the GP paradigm, designed to induce similar search dynamics as those shown by Flat-OE.…”

mentioning

confidence: 99%

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neat Genetic Programming: Controlling bloat naturally

Trujillo

Muñoz

Galván

et al. 2016

Information Sciences

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a b s t r a c tBloat is one of the most widely studied phenomena in Genetic Programming (GP), it is normally defined as the increase in mean program size without a corresponding improvement in fitness. Several theories have been proposed in the specialized GP literature that explain why bloat occurs. In particular, the Crossover-Bias Theory states that the cause of bloat is that the distribution of program sizes during evolution is skewed in a way that encourages bloat to appear, by punishing small individuals and favoring larger ones. Therefore, several bloat control methods have been proposed that attempt to explicitly control the size distribution of programs within the evolving population. This work proposes a new bloat control method called neat-GP, that implicitly shapes the program size distribution during a GP run. neat-GP is based on two key elements: (a) the NeuroEvolution of Augmenting Topologies algorithm (NEAT), a robust heuristic that was originally developed to evolve neural networks; and (b) the Flat Operator Equalization bloat control method, that explicitly shapes the program size distributions toward a uniform or flat shape. Experimental results are encouraging in two domains, symbolic regression and classification of real-world data. neat-GP can curtail the effects of bloat without sacrificing performance, outperforming both standard GP and the Flat-OE method, without incurring in the computational overhead reported by some state-of-the-art bloat control methods. L. Trujillo et al. / Information Sciences 333 (2016) 21-43 problem but also provide insights into the problem domain. Secondly, GP uses a variable length encoding scheme, where the set of candidate solutions contains programs of different size and shape.EC literature contains many examples of the problem solving abilities of GP, that illustrate the flexibility of the search paradigm [8]. Indeed, GP can be understood as a hyper-heuristic, an algorithmic approach for the automatic synthesis of heuristic approaches, a view that has strong theoretical background and real-world applicability [19]. Despite its success, GP is still not used as an off-the-shelf methodology [16], in the way that, for example, Support Vector Machines or Linear Regression are used. This lack of wider acceptance stems from some important pragmatic limitations of the GP approach.In particular, syntactic search can be inefficient, due to its poor local structure and ill-defined fitness landscape (refer to [18] and [22] where the authors reviewed some of the main open issues in GP). Among them, one of the most studied problems is the bloat phenomenon, which occurs when program trees tend to grow unnecessarily large, without a corresponding increase in fitness [21,25]. In some sense, bloat seems to be an unavoidable consequence of the nature of the search space in GP and fitness driven search [11,12]. Moreover, bloat causes several undesirable side effects, since evaluating large programs is more time consuming, and large solutions are more difficult to interpre...

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Section: Introductionsupporting

confidence: 91%

Section: The Main Components In Neatmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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neat Genetic Programming: Controlling bloat naturally

Trujillo

Muñoz

Galván

et al. 2016

Information Sciences

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“…For instance, some researchers apply the LS to the best solution in the population, but [32] [3] 178 13 Chemical analysis of wines Sonar [3] 208 60 Sonar signals off metal cylinder at various angles and conditions Wholesale [3] 440 8 Clients information of wholesale distributor Banknote [3] 1372 5 Image information from genuine & forged banknote-like specimens LSVT [34] 126 309 Signal data from speech rehabilitation treatment on Parkinson patients showed that better results are obtained when it is applied to all the population or a subset of the best solutions in each generation. However, in our case, the number of parameters grows proportionally with the size of the trees, providing incentive to limit the amount of times the LS is applied, especially for larger trees.…”

Section: Integrating Ls Into Gpmentioning

confidence: 99%

A Local Search Approach to Genetic Programming for Binary Classification

Z-Flores

Trujillo

Schütze

et al. 2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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In standard genetic programming (GP), a search is performed over a syntax space defined by the set of primitives, looking for the best expressions that minimize a cost function based on a training set. However, most GP systems lack a numerical optimization method to fine tune the implicit parameters of each candidate solution. Instead, GP relies on more exploratory search operators at the syntax level. This work proposes a memetic GP, tailored for binary classification problems. In the proposed method, each node in a GP tree is weighted by a real-valued parameter, which is then numerically optimized using a continuous transfer function and the Trust Region algorithm is used as a local search method. Experimental results show that potential classifiers produced by GP are improved by the local searcher, and hence the overall search is improved achieving significant performance gains, that are competitive with state-of-the-art methods on well-known benchmarks.

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