A New Implementation of Geometric Semantic GP and Its Application to Problems in Pharmacokinetics

Vanneschi, Leonardo; Castelli, Mauro; Manzoni, Luca; Silva, Sara

doi:10.1007/978-3-642-37207-0_18

Cited by 98 publications

(107 citation statements)

References 11 publications

Supporting

Mentioning

107

Contrasting

Order By: Relevance

“…And the evidence in literature shows that the use of non-optimal combinations of semantic distance and fitness function metrics is common. For instance, [4,32] use an L 1 -semantic distance and a (scaled) L 2 -fitness function, and [13] employ an L 2 -semantic distance and an L 1 -fitness function. With this paper, we hope to help the authors of future studies in GSGP, whether theoretical or more practice-oriented, to choose the better combinations.…”

Section: Discussionmentioning

confidence: 99%

Progress properties and fitness bounds for geometric semantic search operators

Pawlak

Krawiec

2015

Genet Program Evolvable Mach

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Progress properties and fitness bounds for geometric semantic search operators

Pawlak

Krawiec

2015

Genet Program Evolvable Mach

View full text Add to dashboard Cite

show abstract

“…Nevertheless, by changing parameter ms, we are able to tune the ''step'' of the mutation and thus the importance of this perturbation. In this work, as in several previous contributions (like for instance Vanneschi, Castelli, Manzoni, & Silva, 2013), also the random trees T R1 and T R2 used by mutation have been constrained to assume values in ½0; 1, using exactly the same method as for the tree T R used by crossover.…”

Section: Geometric Semantic Mutationmentioning

confidence: 99%

Prediction of the Unified Parkinson’s Disease Rating Scale assessment using a genetic programming system with geometric semantic genetic operators

Castelli

Vanneschi

Silva

2014

Expert Systems with Applications

Self Cite

View full text Add to dashboard Cite

“…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%

“…Therefore, researchers have used semantics to modify traditional genetic operators [24], [26], [27], focusing on improving the semantic diversity of the evolving population. Moreover, other approaches have been proposed to perform the evolutionary search within semantic space explicitly [25], [28], [31]. In general, all of these works have shown improved results compared to standard GP algorithms, mostly evaluated on symbolic regression problems.…”

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%

See 1 more Smart Citation

Searching for novel regression functions

Martínez

Naredo

Trujillo

et al. 2013

2013 IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

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.

show abstract

A New Implementation of Geometric Semantic GP and Its Application to Problems in Pharmacokinetics

Cited by 98 publications

References 11 publications

Progress properties and fitness bounds for geometric semantic search operators

Progress properties and fitness bounds for geometric semantic search operators

Prediction of the Unified Parkinson’s Disease Rating Scale assessment using a genetic programming system with geometric semantic genetic operators

Searching for novel regression functions

Contact Info

Product

Resources

About