Automatic Derivation of Search Objectives for Test-Based Genetic Programming

Foundations of Computing and Decision Sciences

Liskowski

2017

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Abstract. Genetic programming (GP) is a variant of evolutionary algorithm where the entities undergoing simulated evolution are computer programs. A fitness function in GP is usually based on a set of tests, each of which defines the desired output a correct program should return for an exemplary input. The outcomes of interactions between programs and tests in GP can be represented as an interaction matrix, with rows corresponding to programs in the current population and columns corresponding to tests. In previous work, we proposed SFIMX, a method that performs only a fraction of interactions and employs non-negative matrix factorization to estimate the outcomes of remaining ones, shortening GP's runtime. In this paper, we build upon that work and propose three extensions of SFIMX, in which the subset of tests drawn to perform interactions is selected with respect to test difficulty. The conducted experiment indicates that the proposed extensions surpass the original SFIMX on a suite of discrete GP benchmarks.

Section: Discussionsupporting

confidence: 81%

“…Another method that aims at scrutinizing the individual outcomes of interactions and leveraging them for better performance is DOC [18]. In every generation, the algorithm identifies the groups of tests on which the programs in the current population behave similarly and clusters them together to give rise to new search objectives.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Test Selection for Factorization-based Surrogate Fitness in Genetic Programming

Foundations of Computing and Decision Sciences

Liskowski

2017

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“…Discovery of search objectives by clustering (doc) introduced by Krawiec and Liskowski [9] is a method that autonomously derives new search objectives by clustering the outcomes of interactions between programs in the population and the tests. Each derived objective is intended to capture a subset of 'capabilities' exhibited by the programs in the context of other individuals in population.…”

Section: Discovery Of Objectives By Clusteringmentioning

confidence: 99%

“…In this study, we experimentally compare the presumably oldest method of this type, Implicit Fitness Sharing (ifs, [19]) with two approaches proposed recently: discovery of objectives by clustering (doc, [8,12]) and lexicase selection (lex, [4]). Additionally, compared to [8], we consider a new variant of doc that employs a different clustering algorithm. We present the common conceptual framework in Section 2, the compared methods in Section 3, present the results of the experiment involving 18 discrete benchmark in Section 4, to conclude with Section 5.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Semantic-aware Selection Methods in Genetic Programming

Liskowski

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

Helmuth

et al. 2015

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This study investigates the performance of several semanticaware selection methods for genetic programming (GP). In particular, we consider methods that do not rely on complete GP semantics (i.e., a tuple of outputs produced by a program for fitness cases (tests)), but on binary outcome vectors that only state whether a given test has been passed by a program or not. This allows us to relate to test-based problems commonly considered in the domain of coevolutionary algorithms and, in prospect, to address a wider range of practical problems, in particular the problems where desired program output is unknown (e.g., evolving GP controllers). The selection methods considered in the paper include implicit fitness sharing (ifs), discovery of derived objectives (doc), lexicase selection (lex), as well as a hybrid of the latter two. These techniques, together with a few variants, are experimentally compared to each other and to conventional GP on a battery of discrete benchmark problems. The outcomes indicate superior performance of lex and ifs, with some variants of doc showing certain potential.

“…This is the main postulate of behavioral programming [12] which aims at better-informed program synthesis algorithms. Interestingly, the behavioral perspective proves effective also beyond GP, among others in coevolutionary algorithms [11,17].…”

Section: Discussionmentioning

confidence: 99%

Progress properties and fitness bounds for geometric semantic search operators

Pawlak

Genet Program Evolvable Mach

2015

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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.