Offline Design of Interactive Evolutionary Algorithms with Different Genetic Operators at Each Generation

2015 ◽  
pp. 635-646 ◽  
Author(s):  
Hisao Ishibuchi ◽  
Takahiko Sudo ◽  
Koji Ueba ◽  
Yusuke Nojima
Keyword(s):  
Evolutionary Algorithms ◽  
Genetic Operators

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.

Spin-Flip Symmetry and Synchronization

2002 ◽  
Vol 10 (4) ◽  
pp. 317-344 ◽  
Author(s):  
Clarissa Van Hoyweghen ◽  
Bart Naudts ◽  
David E. Goldberg
Keyword(s):  
Evolutionary Algorithms ◽  
Fitness Function ◽  
Premature Convergence ◽  
Search Problem ◽  
Problem Difficulty ◽  
Genetic Operators ◽  
Spin Flip ◽  
Optimal Configurations ◽  
Synchronization Problems

In the context of optimization by evolutionary algorithms (EAs), epistasis, deception, and scaling are well-known examples of problem difficulty characteristics. The presence of one such characteristic in the representation of a search problem indicates a certain type of difficulty the EA is to encounter during its search for globally optimal configurations. In this paper, we claim that the occurrence of symmetry in the representation is another problem difficulty characteristic and discuss one particular form, spin-flip symmetry, characterized by fitness invariant permutations on the alphabet. Its usual effect on unspecialized EAs, premature convergence due to synchronization problems, is discussed in detail. We discuss five different ways to specialize EAs to cope with the symmetry: adapting the genetic operators, changing the fitness function, using a niching technique, using a distributed EA, and attaching a highly redundant genotype-phenotype mapping.

scholarly journals A Mathematical Design of Genetic Operators onGLn(ℤ2)

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yourim Yoon ◽  
Yong-Hyuk Kim
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Linear Algebra ◽  
Binary Representation ◽  
Two Dimensional ◽  
Genetic Operators ◽  
Binary Matrices ◽  
Mathematical Design

We study the space that consists of all nonsingular binary matrices, that is,GLn(ℤ2). The space is quite important in that it is used for the change of basis in binary representation, which is the encoding typically adopted in genetic algorithms. We analyze the properties ofGLn(ℤ2)and theoretically design possible encodings and their corresponding recombination operators for evolutionary algorithms. We present approaches based on elementary matrices of linear algebra as well as typical two-dimensional ones.

Specific Evolutionary Algorithms

1996 ◽  
Author(s):  
Thomas Bäck
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Selection Process ◽  
Evolutionary Programming ◽  
Main Stream ◽  
Genetic Operators ◽  
Optimization Task ◽  
Main Components ◽  
High Level

In this chapter, an outline of an Evolutionary Algorithm is formulated that is sufficiently general to cover at least the three different main stream algorithms mentioned before, namely, Evolution Strategies, Genetic Algorithms, and Evolutionary Programming. As in the previous chapter, algorithms are formulated in a language obtained by mixing pseudocode and mathematical notations, thus allowing for a high-level description which concentrates on the main components. These are: A population of individuals which is manipulated by genetic operators — especially mutation and recombination, but others may also be incorporated — and undergoes a fitness-based selection process, where fitness of an individual depends on its quality with respect to the optimization task.

scholarly journals The Algorithm for Algorithms: An Evolutionary Algorithm Based on Automatic Designing of Genetic Operators

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Dazhi Jiang ◽  
Zhun Fan
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Human Beings ◽  
Genetic Operators ◽  
Problem Space ◽  
Wide Range ◽  
Definition Of ◽  
Solution Accuracy

At present there is a wide range of evolutionary algorithms available to researchers and practitioners. Despite the great diversity of these algorithms, virtually all of the algorithms share one feature: they have been manually designed. A fundamental question is “are there any algorithms that can design evolutionary algorithms automatically?” A more complete definition of the question is “can computer construct an algorithm which will generate algorithms according to the requirement of a problem?” In this paper, a novel evolutionary algorithm based on automatic designing of genetic operators is presented to address these questions. The resulting algorithm not only explores solutions in the problem space like most traditional evolutionary algorithms do, but also automatically generates genetic operators in the operator space. In order to verify the performance of the proposed algorithm, comprehensive experiments on 23 well-known benchmark optimization problems are conducted. The results show that the proposed algorithm can outperform standard differential evolution algorithm in terms of convergence speed and solution accuracy which shows that the algorithm designed automatically by computers can compete with the algorithms designed by human beings.

scholarly journals Modifying Regeneration Mutation and Hybridising Clonal Selection for Evolutionary Algorithms Based Timetabling Tool

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Thatchai Thepphakorn ◽  
Pupong Pongcharoen ◽  
Chris Hicks
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Clonal Selection ◽  
Fractional Factorial ◽  
Hill Climbing ◽  
Clonal Selection Algorithm ◽  
Scheduling Problems ◽  
Genetic Operators ◽  
Course Scheduling ◽  
Elitist Strategy

This paper outlines the development of a new evolutionary algorithms based timetabling (EAT) tool for solving course scheduling problems that include a genetic algorithm (GA) and a memetic algorithm (MA). Reproduction processes may generate infeasible solutions. Previous research has used repair processes that have been applied after a population of chromosomes has been generated. This research developed a new approach which (i) modified the genetic operators to prevent the creation of infeasible solutions before chromosomes were added to the population; (ii) included the clonal selection algorithm (CSA); and the elitist strategy (ES) to improve the quality of the solutions produced. This approach was adopted by both the GA and MA within the EAT. The MA was further modified to include hill climbing local search. The EAT program was tested using 14 benchmark timetabling problems from the literature using a sequential experimental design, which included a fractional factorial screening experiment. Experiments were conducted to (i) test the performance of the proposed modified algorithms; (ii) identify which factors and interactions were statistically significant; (iii) identify appropriate parameters for the GA and MA; and (iv) compare the performance of the various hybrid algorithms. The genetic algorithm with modified genetic operators produced an average improvement of over 50%.

scholarly journals Evolutionary Algorithms for Reinforcement Learning

1999 ◽  
Vol 11 ◽  
pp. 241-276 ◽  
Author(s):  
D. E. Moriarty ◽  
A. C. Schultz ◽  
J. J. Grefenstette
Keyword(s):  
Reinforcement Learning ◽  
Evolutionary Algorithms ◽  
Value Function ◽  
Learning Problems ◽  
Temporal Difference ◽  
Genetic Operators ◽  
Credit Assignment ◽  
Learning Problem ◽  
Difference Methods ◽  
Temporal Difference Methods

There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are well-known examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal difference methods. This article focuses on the application of evolutionary algorithms to the reinforcement learning problem, emphasizing alternative policy representations, credit assignment methods, and problem-specific genetic operators. Strengths and weaknesses of the evolutionary approach to reinforcement learning are presented, along with a survey of representative applications.

Fundamentals of Genetic Programming

2018 ◽  
pp. 1-47
Keyword(s):  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Biological Evolution ◽  
Parallel Execution ◽  
Historical Evolution ◽  
Computer Algorithms ◽  
Genetic Operators ◽  
Fitness Functions ◽  
Termination Criteria ◽  
Scientists And Engineers

In the living world, all species share one very important property, they receive it right after the birth, and it is called the survival instinct. Since the middle of the twentieth century, scientists have been applying the phenomenon in engineering in order to define computer algorithms which follow the principles of biological evolution of species. Eighty years later, scientists and engineers are still applying the phenomenon in order to solve today's most complex and wide variety of problems. This chapter introduces evolutionary algorithms and motivates the reader to start a journey into genetic programming (GP). The chapter starts with the introduction and detailed insights into GP by describing its main parts and terminology in order to define and mimic biological terms with terms in genetic programming. Then the reader is introduced with the historical evolution of GP and the main and the most popular genetic programming variants, it may find dozens of cited references in it. The chapter continues with detailed introduction on the chromosomes, population, initial and selection methods, main genetic operators, various types of fitness functions, termination criteria, etc. Since GP is processor intensive algorithm, it requires parallel execution to increase its performance which is described at the end of the chapter.

Opponent Center-Surround Contrast: Colour to Grey Conversion

2020 ◽  
Vol 2020 (1) ◽  
pp. 105-108
Author(s):  
Ali Alsam
Keyword(s):  
Evolutionary Algorithms ◽  
Three Dimensional ◽  
The State ◽  
Colour Space ◽  
Vision Science ◽  
State Of Art ◽  
Over Time

Vision is the science that informs us about the biological and evolutionary algorithms that our eyes, opticnerves and brains have chosen over time to see. This article is an attempt to solve the problem of colour to grey conversion, by borrowing ideas from vision science. We introduce an algorithm that measures contrast along the opponent colour directions and use the results to combine a three dimensional colour space into a grey. The results indicate that the proposed algorithm competes with the state of art algorithms.

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