Indirect Estimation Of Distribution Algorithms For The Evolution Of Tree-Shaped Structures

Gene Expression Programming ◽

Solution Space ◽

Test Problems ◽

Indirect Estimation ◽

Lawn Mower ◽

N Gram ◽

This thesis explores indirect estimation of distribution algorithms (IEDAs) for the evolution of tree structured expressions. Unlike conventional estimation of distribution algorithms, IEDAs maintain a distribution of the genotype space and indirectly search the solution space by performing a genotype-to-phenotype mapping. In this work we introduce two IEDAs named PDPE and N-gram GEP. PDPE induces a population of programs, encoded as fixed-length gene expression programming (GEP) chromosomes, by iteratively refining and randomly sampling a probability distribution of program instructions. N-gram GEP attempts to capture regularities in GEP chromosomes by sampling the probability distribution of triplet of instructions (3-grams). We tested the performance of these systems using a variety of non-trivial test problems, such as symbolic regression and the lawn-mower problem. We compared PDPE and N-gram GEP with their predecessors, probabilistic incremental program evolution (PIPE) and N-gram GP, and the canonical GEP algorithm. The results proved that our methodology is more efficient than PIPE and the canonical GEP algorithm.

Bayesian Networks Model for Estimation of Distribution Algorithms

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.3594 ◽

2014 ◽

Vol 926-930 ◽

pp. 3594-3597

Author(s):

Cai Chang Ding ◽

Wen Xiu Peng ◽

Wei Ming Wang

Keyword(s):

Bayesian Networks ◽

Evolutionary Computation ◽

Joint Probability ◽

Joint Probability Distribution ◽

Previous Generation ◽

Mutation Operators ◽

Estimation of Distribution Algorithms (EDAs) are a set of algorithms that belong to the field of Evolutionary Computation. In EDAs there are neither crossover nor mutation operators. Instead, the new population of individuals is sampled from a probability distribution, which is estimated from a database that contains the selected individuals from the previous generation. Thus, the interrelations between the different variables that represent the individuals may be explicitly expressed through the joint probability distribution associated with the individuals selected at each generation.

Combinational Circuit Design with Estimation of Distribution Algorithms

Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery ◽

10.4018/978-1-60566-705-8.ch012 ◽

2010 ◽

pp. 281-297

Author(s):

Sergio Ivvan Valdez Peña ◽

Arturo Hernández Aguirre ◽

Salvador Botello Rionda ◽

Cyntia Araiza Delgado

Keyword(s):

Circuit Design ◽

Probability Model ◽

Combinational Circuit ◽

Conditional Probability Distribution ◽

Data Dependencies ◽

Distribution Algorithms ◽

Design Experiments

The authors introduce new approaches for the combinational circuit design based on Estimation of Distribution Algorithms. In this paradigm, the structure and data dependencies embedded in the data (population of candidate circuits) are modeled by a conditional probability distribution function. The new population is simulated from the probability model thus inheriting the dependencies. The authors explain the procedure to build an approximation of the probability distribution through two approaches: polytrees and Bayesian networks. A set of circuit design experiments is performed and a comparison with evolutionary approaches is reported.

Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference - GECCO '13 ◽

Design of test problems for discrete estimation of distribution algorithms

10.1145/2463372.2463419 ◽

2013 ◽

Author(s):

Shih-Ming Wang ◽

Jie-Wei Wu ◽

Wei-Ming Chen ◽

Tian-Li Yu

Keyword(s):

Test Problems ◽

Distribution Algorithms ◽

Discrete Estimation

Research on Methodology of Evolutionary Computing

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.490-495.524 ◽

2012 ◽

Vol 490-495 ◽

pp. 524-528

Author(s):

Jun Fei Zhuo ◽

Xing He Wu ◽

Guan Zhao Wu ◽

Min Yao

Keyword(s):

Gene Expression ◽

Particle Swarm Optimization ◽

Differential Evolution ◽

Computational Intelligence ◽

Gene Expression Programming ◽

Evolutionary Computing ◽

Swarm Optimization ◽

Evolutionary computing is one of the important branches in computational intelligence. This paper mainly introduces four new branches of the evolutionary computation, i.e. Gene Expression Programming (GEP), Particle Swarm Optimization (PSO), Differential Evolution (DE) and Estimation of Distribution Algorithms (EDA).

Cyber-EDA: Estimation of Distribution Algorithms with Adaptive Memory Programming

Mathematical Problems in Engineering ◽

10.1155/2013/132697 ◽

2013 ◽

Vol 2013 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Peng-Yeng Yin ◽

Hsi-Li Wu

Keyword(s):

Evolutionary Algorithms ◽

Probability Model ◽

Global Optimum ◽

Experimental Result ◽

Adaptive Memory ◽

Distribution Algorithms ◽

Iterative Filtering

The estimation of distribution algorithm (EDA) aims to explicitly model the probability distribution of the quality solutions to the underlying problem. By iterative filtering for quality solution from competing ones, the probability model eventually approximates the distribution of global optimum solutions. In contrast to classic evolutionary algorithms (EAs), EDA framework is flexible and is able to handle inter variable dependence, which usually imposes difficulties on classic EAs. The success of EDA relies on effective and efficient building of the probability model. This paper facilitates EDA from the adaptive memory programming (AMP) domain which has developed several improved forms of EAs using the Cyber-EA framework. The experimental result on benchmark TSP instances supports our anticipation that the AMP strategies can enhance the performance of classic EDA by deriving a better approximation for the true distribution of the target solutions.

Minimization of the Total Traveling Distance and Maximum Distance by Using a Transformed-Based Encoding EDA to Solve the Multiple Traveling Salesmen Problem

Mathematical Problems in Engineering ◽

10.1155/2015/640231 ◽

2015 ◽

Vol 2015 ◽

pp. 1-13 ◽

Cited By ~ 3

Author(s):

S. H. Chen

Keyword(s):

Genetic Algorithm ◽

Optimization Problem ◽

Optimization Problems ◽

Solution Space ◽

Maximum Distance ◽

Hard Problems ◽

Estimation of distribution algorithms (EDAs) have been used to solve numerous hard problems. However, their use with in-group optimization problems has not been discussed extensively in the literature. A well-known in-group optimization problem is the multiple traveling salesmen problem (mTSP), which involves simultaneous assignment and sequencing procedures and are shown in different forms. This paper presents a new algorithm, namedEDAMLA, which is based on self-guided genetic algorithm with a minimum loading assignment (MLA) rule. This strategy uses the transformed-based encoding approach instead of direct encoding. The solution space of the proposed method is onlyn!. We compare the proposed algorithm against the optimal direct encoding technique, the two-part encoding genetic algorithm, and, in experiments on 34 TSP instances drawn from the TSPLIB, find that its solution space isn!n-1m-1. The scale of the experiments exceeded that presented in prior studies. The results show that the proposed algorithm was superior to the two-part encoding genetic algorithm in terms of minimizing the total traveling distance. Notably, the proposed algorithm did not cause a longer traveling distance when the number of salesmen was increased from 3 to 10. The results suggest that EDA researchers should employ the MLA rule instead of direct encoding in their proposed algorithms.