Preliminary Study of Bloat in Genetic Programming with Behavior-Based Search

Trujillo, Leonardo; Naredo, Enrique; Martínez, Yuliana

doi:10.1007/978-3-319-01128-8_19

Cited by 10 publications

(9 citation statements)

References 34 publications

(57 reference statements)

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“…the search for novelty leads towards quality when problem difficulty is, in some sense, large. These results are indeed promising, another encouraging reason that justifies why behavior-based search strategies should be further explored [20], [21], [33].…”

Section: Introductionmentioning

confidence: 76%

“…The reason for this is actually very intuitive, given that the main cause for bloat is the bias introduced by the search for better fitness, what is known as the fitness-causes-theory [43], [44]. Recent studies of NS applied to pattern recognition problems [20], [21] has produced results that suggest that the elimination of an explicit fitness function can curtail bloating during a GP run [33]. However, since the algorithm and behavioral descriptors used in [20], [21], [33] differ on some key aspects with respect to the proposal described in this paper, then further analysis and experimental validations is required.…”

Section: Discussionmentioning

confidence: 99%

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Searching for novel regression functions

Martínez

Naredo

Trujillo

et al. 2013

2013 IEEE Congress on Evolutionary Computation

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Abstract-The objective function is the core element in most search algorithms that are used to solve engineering and scientific problems, referred to as the fitness function in evolutionary computation. Some researchers have attempted to bridge this difference by reducing the need for an explicit fitness function. A noteworthy example is the novelty search (NS) algorithm, that substitutes fitness with a measure of uniqueness, or novelty, that each individual introduces into the search. NS employs the concept of behavioral space, where each individual is described by a domain-specific descriptor that captures the main features of an individual's performance. However, defining a behavioral descriptor is not trivial, and most works with NS have focused on robotics. This paper is an extension of recent attempts to expand the application domain of NS. In particular, it represents the first attempt to apply NS on symbolic regression with genetic programming (GP). The relationship between the proposed NS algorithm and recent semantics-based GP algorithms is explored. Results are encouraging and consistent with recent findings, where NS achieves below average performance on easy problems, and achieves very good performance on hard problems. In summary, this paper presents the first attempt to apply NS on symbolic regression, a continuation of recent research devoted at extending the domain of competence for behavior-based search.

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Section: Introductionmentioning

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Searching for novel regression functions

Martínez

Naredo

Trujillo

et al. 2013

2013 IEEE Congress on Evolutionary Computation

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Section: Introductionmentioning

confidence: 99%

“…Moreover, bloat causes several undesirable side effects, since evaluating large programs is more time consuming, and large solutions are more difficult to interpret. Therefore, multiple approaches have been studied to deal with bloat, ranging from modifications of the basic search operators up to investigating the use of different search spaces, such as semantic space [6,33] and behavioral space [30].This paper presents a novel approach toward bloat control, that leverages the insights of recent studies [23] and an algorithm originally developed for neuroevolution [28]. Silva [23] suggests that a powerful bloat control strategy is to induce a uniform distribution of program sizes within the evolving population.…”

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confidence: 99%

“…Indeed, stating that the search for fitness is the main cause of bloat is by now uncontroversial, since it is basically the underlying factor in all major bloat theories [25]. Moreover, recent works suggest that a GP search that does not consider fitness explicitly can in fact avoid bloat altogether, by searching for novelty instead of solution quality [13,30].Currently, one of the most useful bloat theories is the crossover bias theory (CBT) [20]. Focusing on canonical GP, the CBT states that bloat is produced by the effect that subtree crossover has on the distribution of program size.…”

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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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