Semantically driven mutation in genetic programming

Beadle, Lawrence; Johnson, Colin G.

doi:10.1109/cec.2009.4983099

Cited by 52 publications

(47 citation statements)

References 17 publications

(18 reference statements)

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“…Therefore, semantics adds another space of analysis in which the search is being conducted, along with genotypic, phenotypic and objective (fitness) space, we can also consider semantic space; where a many-to-one relation will usually exists between genotypic (phenotypic) space and semantic space. Researchers have used semantics to improve GP in different ways, such as modifying traditional genetic operators to improve the semantic diversity of the evolving population [1,2,36], or by explicitly performing evolution within semantic space [12,21]. In general, all of these works have shown improved results using a canonical GP as a control method, mostly on symbolic regression problems.…”

Section: Behavior-based Searchmentioning

confidence: 99%

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

Trujillo

Naredo

Martínez

2013

EVOLVE - A Bridge Between Probability, Set Oriented Numerics, and Evolutionary Computation IV

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Abstract. Bloat is one of the most interesting theoretical problems in genetic programming (GP), and one of the most important pragmatic limitations in the development of real-world GP solutions. Over the years, many theories regarding the causes of bloat have been proposed and a variety of bloat control methods have been developed. It seems that one of the underlying causes of bloat is the search for fitness; as the fitness-causes-bloat theory states, selective bias towards fitness seems to unavoidably lead the search towards programs with a large size. Intuitively, however, abandoning fitness does not appear to be an option. This paper, studies a GP system that does not require an explicit fitness function, instead it relies on behavior-based search, where programs are described by the behavior they exhibit and selective pressure is biased towards unique behaviors using the novelty search algorithm. Initial results are encouraging, the average program size of the evolving population does not increase with novelty search; i.e., bloat is avoided by focusing on novelty instead of quality.

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Section: Behavior-based Searchmentioning

confidence: 99%

“…Semantics in GP describes the performance of a program with the raw output vector computed over all fitness cases [21,1,2,12,20,36]. Given a set of n fitness cases, the semantics of a program K is the corresponding output vector it produces y ∈ R n .…”

Section: Behavior-based Searchmentioning

confidence: 99%

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

Trujillo

Naredo

Martínez

2013

EVOLVE - A Bridge Between Probability, Set Oriented Numerics, and Evolutionary Computation IV

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“…Semantics in GP, and its corresponding semantic space, describes the performance of an individual using its raw output vector computed over all the fitness cases of a problem [23], [24], [25], [26], [27], [28], [31]. In other words, given a set of n fitness cases the semantics of a program K is the corresponding set of outputs it produces, normally expressed as a vector y ∈ R n .…”

Section: A Semantics In Genetic Programmingmentioning

confidence: 99%

“…Such problems have been extensively studied and analyzed by the GP community [22]. Recently, symbolic regression GP systems have been improved by incorporating the concept of semantics [23], [24], [25], [26], [27], [28], [29], [30], [31]. On the other hand, the present work takes an approach based on behaviors, the difference between both approaches is carefully discussed below.…”

Section: Introductionmentioning

confidence: 99%

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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“…By nature, program semantics is more detailed than the scalar fitness commonly used in GP, and in effect offers a more informative guidance for program synthesis. This virtue of SGP has brought substantial performance gains in numerous works [1,3,13,19,23,27,30,31].…”

Section: Introductionmentioning

confidence: 99%

Progress properties and fitness bounds for geometric semantic search operators

Pawlak

Krawiec

2015

Genet Program Evolvable Mach

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Metrics are essential for geometric semantic genetic programming. On one hand, they structure the semantic space and govern the behavior of geometric search operators; on the other, they determine how fitness is calculated. The interactions between these two types of metrics are an important aspect that to date was largely neglected. In this paper, we investigate these interactions and analyze their consequences. We provide a systematic theoretical analysis of the properties of abstract geometric semantic search operators under Minkowski metrics of arbitrary order. For nine combinations of popular metrics (city-block, Euclidean, and Chebyshev) used in fitness functions and of search operators, we derive pessimistic bounds on fitness change. We also define three types of progress properties (weak, potential, and strong) and verify them for operators under those metrics. The analysis allows us to determine the combinations of metrics that are most attractive in terms of progress properties and deterioration bounds.

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Semantically driven mutation in genetic programming

Cited by 52 publications

References 17 publications

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

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

Searching for novel regression functions

Progress properties and fitness bounds for geometric semantic search operators

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