scholarly journals Evolving Solutions to Community-Structured Satisfiability Formulas

2019 ◽  
Vol 33 ◽  
pp. 2346-2353
Author(s):  
Frank Neumann ◽  
Andrew M. Sutton
Keyword(s):  
Community Structure ◽  
Evolutionary Algorithms ◽  
Objective Function ◽  
Search Space ◽  
Propositional Satisfiability ◽  
Satisfying Assignment ◽  
Distance Correlation ◽  
Strong Signal ◽  
Fitness Distance Correlation ◽  
Correlation Properties

We study the ability of a simple mutation-only evolutionary algorithm to solve propositional satisfiability formulas with inherent community structure. We show that the community structure translates to good fitness-distance correlation properties, which implies that the objective function provides a strong signal in the search space for evolutionary algorithms to locate a satisfying assignment efficiently. We prove that when the formula clusters into communities of size s ∈ ω(logn) ∩O(nε/(2ε+2)) for some constant 0

A New Method for Modeling the Behavior of Finite Population Evolutionary Algorithms

2010 ◽  
Vol 18 (3) ◽  
pp. 451-489 ◽  
Author(s):  
Tatsuya Motoki
Keyword(s):  
Markov Chain ◽  
Evolutionary Algorithms ◽  
Finite Population ◽  
Fitness Landscape ◽  
Markov Chain Model ◽  
Search Space ◽  
Chain Model ◽  
Transition Matrices ◽  
Memory Space ◽  
Exact Model

As practitioners we are interested in the likelihood of the population containing a copy of the optimum. The dynamic systems approach, however, does not help us to calculate that quantity. Markov chain analysis can be used in principle to calculate the quantity. However, since the associated transition matrices are enormous even for modest problems, it follows that in practice these calculations are usually computationally infeasible. Therefore, some improvements on this situation are desirable. In this paper, we present a method for modeling the behavior of finite population evolutionary algorithms (EAs), and show that if the population size is greater than 1 and much less than the cardinality of the search space, the resulting exact model requires considerably less memory space for theoretically running the stochastic search process of the original EA than the Nix and Vose-style Markov chain model. We also present some approximate models that use still less memory space than the exact model. Furthermore, based on our models, we examine the selection pressure by fitness-proportionate selection, and observe that on average over all population trajectories, there is no such strong bias toward selecting the higher fitness individuals as the fitness landscape suggests.

scholarly journals Fitness Distance Correlation in Structural Mutation Genetic Programming

2003 ◽  
pp. 455-464 ◽  
Author(s):  
Leonardo Vanneschi ◽  
Marco Tomassini ◽  
Philippe Collard ◽  
Manuel Clergue
Keyword(s):  
Genetic Programming ◽  
Distance Correlation ◽  
Structural Mutation ◽  
Fitness Distance Correlation

Genetic Diversity as an Objective in Multi-Objective Evolutionary Algorithms

2003 ◽  
Vol 11 (2) ◽  
pp. 151-167 ◽  
Author(s):  
Andrea Toffolo ◽  
Ernesto Benini
Keyword(s):  
Genetic Diversity ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Selection Pressure ◽  
Evaluation Method ◽  
State Of The Art ◽  
Search Space ◽  
Test Problems ◽  
Multi Objective ◽  
Diversity Evaluation

A key feature of an efficient and reliable multi-objective evolutionary algorithm is the ability to maintain genetic diversity within a population of solutions. In this paper, we present a new diversity-preserving mechanism, the Genetic Diversity Evaluation Method (GeDEM), which considers a distance-based measure of genetic diversity as a real objective in fitness assignment. This provides a dual selection pressure towards the exploitation of current non-dominated solutions and the exploration of the search space. We also introduce a new multi-objective evolutionary algorithm, the Genetic Diversity Evolutionary Algorithm (GDEA), strictly designed around GeDEM and then we compare it with other state-of-the-art algorithms on a well-established suite of test problems. Experimental results clearly indicate that the performance of GDEA is top-level.

Improving In-Flight Learning in a Flapping Wing Micro Air Vehicle

2016 ◽  
Vol 4 (1) ◽  
pp. 62-75
Author(s):  
Monica Sam ◽  
Sanjay Boddhu ◽  
Kayleigh Duncan ◽  
Hermanus Botha ◽  
John Gallagher
Keyword(s):  
Evolutionary Algorithms ◽  
Control Problem ◽  
Flight Control ◽  
Search Space ◽  
Flapping Wing ◽  
Micro Air Vehicle ◽  
Flight Trajectory ◽  
Air Vehicle ◽  
Major Criterion ◽  
Sampling Approach

Much effort has gone into improving the performance of evolutionary algorithms that augment traditional control in a Flapping Wing Micro Air Vehicle. An EA applied to such a vehicle in flight is expected to evolve solutions quickly to prevent disruptions in following the desired flight trajectory. Time to evolve solutions therefore is a major criterion by which performance of an algorithm is evaluated. This paper presents results of applying an assortment of different evolutionary algorithms to the problem. This paper also presents some discussion on which choices for representation and algorithm parameters would be optimal for the flight control problem and the rationale behind it. The authors also present a guided sampling approach of the search space to make use of the redundancy of workable solutions found in the search space. This approach has been demonstrated to improve learning times when applied to the problem.

scholarly journals Evolutionary Algorithms, Homomorphous Mappings, and Constrained Parameter Optimization

1999 ◽  
Vol 7 (1) ◽  
pp. 19-44 ◽  
Author(s):  
Slawomir Koziel ◽  
Zbigniew Michalewicz
Keyword(s):  
Evolutionary Algorithms ◽  
Parameter Optimization ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Search Space ◽  
Test Cases ◽  
Nonlinear Constraints ◽  
New Approach ◽  
Survey Papers ◽  
Dimensional Cube

During the last five years, several methods have been proposed for handling nonlinear constraints using evolutionary algorithms (EAs) for numerical optimization problems. Recent survey papers classify these methods into four categories: preservation of feasibility, penalty functions, searching for feasibility, and other hybrids. In this paper we investigate a new approach for solving constrained numerical optimization problems which incorporates a homomorphous mapping between n-dimensional cube and a feasible search space. This approach constitutes an example of the fifth decoder-based category of constraint handling techniques. We demonstrate the power of this new approach on several test cases and discuss its further potential.

Fitness Distance Correlation(FDC) As A Hardness Prediction For University Course Timetabling Problem

2008 ◽  
Vol 49 (1) ◽  
Author(s):  
Abu Bakar Md Sultan ◽  
Ramlan Mahmod ◽  
Md Nasir Sulaiman ◽  
Mohd Rizam Abu Bakar ◽  
Mohd Taufik Abdullah
Keyword(s):  
Distance Correlation ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
University Course Timetabling ◽  
Hardness Prediction ◽  
Fitness Distance Correlation ◽  
Course Timetabling Problem ◽  
University Course
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