Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning

2012 ◽  
Author(s):  
Arina Buzdalova ◽  
Maxim Buzdalov
Keyword(s):  
Reinforcement Learning ◽  
Evolutionary Algorithms ◽  
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

scholarly journals BOLeRo: Behavior optimization and learning for robots

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142091374
Author(s):  
Alexander Fabisch ◽  
Malte Langosz ◽  
Frank Kirchner
Keyword(s):  
Reinforcement Learning ◽  
Evolutionary Algorithms ◽  
Open Source ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Robotic Systems ◽  
Policy Search ◽  
Behavior Learning ◽  
And Behavior

Reinforcement learning and behavior optimization are becoming more and more popular in the field of robotics because algorithms are mature enough to tackle real problems in this domain. Robust implementations of state-of-the-art algorithms are often not publicly available though, and experiments are hardly reproducible because open-source implementations are often not available or are still in a stage of research code. Consequently, often it is infeasible to deploy these algorithms on robotic systems. BOLeRo closes this gap for policy search and evolutionary algorithms by delivering open-source implementations of behavior learning algorithms for robots. It is easy to integrate in robotic middlewares and it can be used to compare methods and develop prototypes in simulation.

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.

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