Now a days space vehicles and other electronic hardware also demands that the architectures should be small, speed in operation, low power consumption, small in area and be reconfigurable in unexpected environments. The evolvable hardware (EHW) or evolutionary algorithm used to design the desired circuit automatically. Genetic algorithm is the commonly used evolutionary algorithm. EHW, because ...

According to the No Free Lunch (NFL) theorems all blackbox algorithms perform equally well when compared over the entire set of optimization problems. An important problem related to NFL is finding a test problem for which a given algorithm is better than another given algorithm. Of high interest is finding a function for which Random Search is better than another standard evolutionary algorith...

This paper proposes a novel optimization algorithm called Cellular Probabilistic Optimization Algorithms (CPOA) based on the probabilistic representation of solutions for real coded problems. In place of binary integers, the basic unit of information here is a probability density function. This probabilistic coding allows superposition of states for a more efficient algorithm. This probabilisti...

This study proposes a modified version of cultural algorithms (CAs) which benefits from rule-based system for influence function. This rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. This is important to use appropriate influence function to apply to a specific individual, regarding to its role in the search process. This rule ...

Optimizing the database queries is one of hard research problems. Exhaustive search techniques like dynamic programming is suitable for queries with a few relations, but by increasing the number of relations in query, much use of memory and processing is needed, and the use of these methods is not suitable, so we have to use random and evolutionary methods. The use of evolutionary methods, beca...

The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as Vector Optimization Problems (VOPs)) has attracted much attention recently. Being population based approaches, EAs offer a means to find a group of pareto–optimal solutions in a single run. Differential Evolution (DE) is an EA that was developed to handle optimization problems over continuous domains. ...

This paper presents a differential evolution algorithm to solve the permutaion flowshop sequencing problem with makespan criterion. Differential evolution is one of the latest evolutionary optmization algorithm applied to continuous optimization problems where members of population use chromosomes based on floating-point numbers to represent candidate solutions. In this paper we also present a ...

The Parameter-less Genetic Algorithm was first presented by Harik and Lobo in 1999 as an alternative to the usual trial-and-error method of finding, for each given problem, an acceptable set-up of the parameter values of the genetic algorithm. Since then, the same strategy has been successfully applied to create parameter-less versions of other population-based search algorithms such as the Ext...