Combinational Circuit Design with Estimation of Distribution Algorithms

2010 ◽  
pp. 281-297
Author(s):  
Sergio Ivvan Valdez Peña ◽  
Arturo Hernández Aguirre ◽  
Salvador Botello Rionda ◽  
Cyntia Araiza Delgado
Keyword(s):  
Probability Distribution ◽  
Circuit Design ◽  
Probability Model ◽  
Estimation Of Distribution Algorithms ◽  
Combinational Circuit ◽  
Conditional Probability Distribution ◽  
Data Dependencies ◽  
Estimation Of Distribution ◽  
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.

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

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Peng-Yeng Yin ◽  
Hsi-Li Wu
Keyword(s):  
Probability Distribution ◽  
Evolutionary Algorithms ◽  
Probability Model ◽  
Global Optimum ◽  
Experimental Result ◽  
Estimation Of Distribution Algorithms ◽  
Adaptive Memory ◽  
Estimation Of Distribution ◽  
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.

Bayesian Networks Model for Estimation of Distribution Algorithms

2014 ◽  
Vol 926-930 ◽  
pp. 3594-3597
Author(s):  
Cai Chang Ding ◽  
Wen Xiu Peng ◽  
Wei Ming Wang
Keyword(s):  
Probability Distribution ◽  
Bayesian Networks ◽  
Evolutionary Computation ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Estimation Of Distribution Algorithms ◽  
Previous Generation ◽  
Mutation Operators ◽  
Estimation Of Distribution ◽  
Distribution Algorithms

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.

Drift and Scaling in Estimation of Distribution Algorithms

2005 ◽  
Vol 13 (1) ◽  
pp. 99-123 ◽  
Author(s):  
J. L. Shapiro
Keyword(s):  
Population Size ◽  
Probability Model ◽  
Search Space ◽  
System Size ◽  
Estimation Of Distribution Algorithms ◽  
Square Root ◽  
Problem Size ◽  
Probability Models ◽  
Estimation Of Distribution ◽  
Distribution Algorithms

This paper considers a phenomenon in Estimation of Distribution Algorithms (EDA) analogous to drift in population genetic dynamics. Finite population sampling in selection results in fluctuations which get reinforced when the probability model is updated. As a consequence, any probability model which can generate only a single set of values with probability 1 can be an attractive fixed point of the algorithm. To avoid this, parameters of the algorithm must scale with the system size in strongly problem-dependent ways, or the algorithm must be modified. This phenomenon is shown to hold for general EDAs as a consequence of the lack of ergodicity and irreducibility of the Markov chain on the state of probability models. It is illustrated in the case of UMDA, in which it is shown that the global optimum is only found if the population size is sufficiently large. For the needle-in-a haystack problem, the population size must scale as the square-root of the size of the search space. For the one-max problem, the population size must scale as the square-root of the problem size.

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

2021 ◽  
Author(s):  
Elmira Ghoulbeigi
Keyword(s):  
Probability Distribution ◽  
Gene Expression Programming ◽  
Solution Space ◽  
Test Problems ◽  
Estimation Of Distribution Algorithms ◽  
Indirect Estimation ◽  
Lawn Mower ◽  
Estimation Of Distribution ◽  
N Gram ◽  
Distribution Algorithms

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.

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

2021 ◽  
Author(s):  
Elmira Ghoulbeigi
Keyword(s):  
Probability Distribution ◽  
Gene Expression Programming ◽  
Solution Space ◽  
Test Problems ◽  
Estimation Of Distribution Algorithms ◽  
Indirect Estimation ◽  
Lawn Mower ◽  
Estimation Of Distribution ◽  
N Gram ◽  
Distribution Algorithms

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.

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