A Glance on Performance of Fitness Functions Toward Evolutionary Algorithms in Mutation Testing

2020 ◽  
pp. 59-75
Author(s):  
Reza Ebrahimi Atani ◽  
Hasan Farzaneh ◽  
Sina Bakhshayeshi
Keyword(s):  
Evolutionary Algorithms ◽  
Mutation Testing ◽  
Fitness Functions

scholarly journals Independence Conservation and Evolutionary Algorithms

2020 ◽  
Vol 2 (1) ◽  
pp. 32-35
Author(s):  
Eric Holloway
Keyword(s):  
Information Theory ◽  
Evolutionary Algorithms ◽  
Algorithmic Information Theory ◽  
Fitness Functions ◽  
Information Law ◽  
Algorithmic Information

Leonid Levin developed the first stochastic conservation of information law, describing it as "torturing an uninformed witness cannot give information about the crime."  Levin's law unifies both the deterministic and stochastic cases of conservation of information.  A proof of Levin's law from Algorithmic Information Theory is given as well as a discussion of its implications in evolutionary algorithms and fitness functions.

Knowledge-Intensive Evolutionary Algorithms for Solving a Healthcare Fleet Optimization Problem

2018 ◽  
pp. 192-223
Author(s):  
Carlos Adrian Catania ◽  
Cecilia Zanni-Merk ◽  
François de Bertrand de Beuvron ◽  
Pierre Collet
Keyword(s):  
Evolutionary Algorithms ◽  
Knowledge Engineering ◽  
Domain Ontology ◽  
Real Problem ◽  
Fitness Functions ◽  
Evaluation Functions ◽  
Mutation Operators ◽  
Knowledge Intensive ◽  
Definition Of ◽  
Crossover And Mutation

In this chapter, the authors show how knowledge engineering techniques can be used to guide the definition of evolutionary algorithms (EA) for problems involving a large amount of structured data, through the resolution of a real problem. Various representations of the fitness functions, the genome, and mutation/crossover operators adapted to different types of problems (routing, scheduling, etc.) have been proposed in the literature. However, real problems including specific constraints (legal restrictions, specific usages, etc.) are often overlooked by the proposed generic models. To ensure that these constraints are effectively considered, the authors propose a methodology based on the structuring of the conceptual model underlying the problem, as a labelled domain ontology suitable for optimization by EA. The authors show that a precise definition of the knowledge model with a labelled domain ontology can be used to describe the chromosome, the evaluation functions, and the crossover and mutation operators. The authors show the details for a real implementation and some experimental results.

Accelerating floating-point fitness functions in evolutionary algorithms: a FPGA-CPU-GPU performance comparison

2011 ◽  
Vol 12 (4) ◽  
pp. 403-427 ◽  
Author(s):  
Juan A. Gomez-Pulido ◽  
Miguel A. Vega-Rodriguez ◽  
Juan M. Sanchez-Perez ◽  
Silvio Priem-Mendes ◽  
Vitor Carreira
Keyword(s):  
Evolutionary Algorithms ◽  
Performance Comparison ◽  
Floating Point ◽  
Fitness Functions

Ontologies to Lead Knowledge Intensive Evolutionary Algorithms

2016 ◽  
Vol 7 (1) ◽  
pp. 78-100 ◽  
Author(s):  
Carlos Adrian Catania ◽  
Cecilia Zanni-Merk ◽  
François de Bertrand de Beuvron ◽  
Pierre Collet
Keyword(s):  
Evolutionary Algorithms ◽  
Knowledge Engineering ◽  
Domain Ontology ◽  
Real Problem ◽  
Genetic Operators ◽  
Fitness Functions ◽  
Generic Models ◽  
Intractable Problems ◽  
Knowledge Intensive ◽  
Definition Of

Evolutionary Algorithms (EA) have proven to be very effective in optimizing intractable problems in many areas. However, real problems including specific constraints are often overlooked by the proposed generic models. The authors' goal here is to show how knowledge engineering techniques can be used to guide the definition of Evolutionary Algorithms (EA) for problems involving a large amount of structured data, through the resolution of a real problem. They propose a methodology based on the structuring of the conceptual model underlying the problem, in the form of a labelled domain ontology suitable for optimization by EA. The case studyfocuses on the logistics involved in the transportation of patients. Although this problem belongs to the well-known family of Vehicle Routing Problems, its specificity comes from the data and constraints (cost, legal and health considerations) that must be taken into account. The precise definition of the knowledge model with thelabelled domain ontology permits the formal description of the chromosome, the fitness functions and the genetic operators.

Online Discovery of Search Objectives for Test-Based Problems

2017 ◽  
Vol 25 (3) ◽  
pp. 375-406 ◽  
Author(s):  
Paweł Liskowski ◽  
Krzysztof Krawiec
Keyword(s):  
Evolutionary Algorithms ◽  
Search Algorithm ◽  
Premature Convergence ◽  
Multiple Tests ◽  
Multi Objective ◽  
Coevolutionary Algorithms ◽  
Fitness Functions ◽  
Candidate Solution ◽  
Complete Order ◽  
Theoretical Evidence

In test-based problems, commonly approached with competitive coevolutionary algorithms, the fitness of a candidate solution is determined by the outcomes of its interactions with multiple tests. Usually, fitness is a scalar aggregate of interaction outcomes, and as such imposes a complete order on the candidate solutions. However, passing different tests may require unrelated “skills,” and candidate solutions may vary with respect to such capabilities. In this study, we provide theoretical evidence that scalar fitness, inherently incapable of capturing such differences, is likely to lead to premature convergence. To mitigate this problem, we propose disco, a method that automatically identifies the groups of tests for which the candidate solutions behave similarly and define the above skills. Each such group gives rise to a derived objective, and these objectives together guide the search algorithm in multi-objective fashion. When applied to several well-known test-based problems, the proposed approach significantly outperforms the conventional two-population coevolution. This opens the door to efficient and generic countermeasures to premature convergence for both coevolutionary and evolutionary algorithms applied to problems featuring aggregating fitness functions.

Energy-aware load balancing of parallel evolutionary algorithms with heavy fitness functions in heterogeneous CPU-GPU architectures

2018 ◽  
Vol 31 (6) ◽  
pp. e4688 ◽  
Author(s):  
Juan José Escobar ◽  
Julio Ortega ◽  
Antonio Francisco Díaz ◽  
Jesús González ◽  
Miguel Damas
Keyword(s):  
Load Balancing ◽  
Evolutionary Algorithms ◽  
Energy Aware ◽  
Fitness Functions ◽  
Parallel Evolutionary Algorithms ◽  
Gpu Architectures

Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art

2000 ◽  
Vol 8 (2) ◽  
pp. 125-147 ◽  
Author(s):  
David A. Van Veldhuizen ◽  
Gary B. Lamont
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Fitness Functions ◽  
Fitness Sharing ◽  
Pareto Ranking ◽  
Multiobjective Optimization Problems ◽  
Conflicting Objectives ◽  
Generic Class ◽  
Mating Restriction ◽  
Future Work

Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade, a variety of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define multiobjective optimization problems and certain related concepts, present an MOEA classification scheme, and evaluate the variety of contemporary MOEAs. Current MOEA theoretical developments are evaluated; specific topics addressed include fitness functions, Pareto ranking, niching, fitness sharing, mating restriction, and secondary populations. Since the development and application of MOEAs is a dynamic and rapidly growing activity, we focus on key analytical insights based upon critical MOEA evaluation of current research and applications. Recommended MOEA designs are presented, along with conclusions and recommendations for future work.

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