Motivated by biological inspiration and the issue of code disruption, we develop a new form of LGP called Parallel LGP (PLGP). PLGP programs consists of n lists of instructions. These lists are executed in parallel, after which the resulting vectors are combined to produce program output. PGLP limits the disruptive effects of crossover and mutation, which allows PLGP to significantly outperform...

Abstract In supervised learning, feature selection methods identify the most relevant predictors to include in a model. For linear models, inclusion or exclusion of each variable may be represented as vector bits playing role genetic material that defines Genetic algorithms reproduce strategies natural on population models best. We derive distribution importance scores for parallel under null h...

nowadays network-on-chips is used instead of system-on-chips for better performance. this paper presents a new algorithm to find a shorter path, and shows that genetic algorithm is a potential technique for solving routing problem for mesh topology in on-chip-network.

in this paper, we develop a capacitated location-covering model considering interval values for demand and service parameters. we also consider flexibility on distance standard for covering demand nodes by the servers. we use the satisfaction degree to represent the constraint of service capacity. the proposed model belongs to the class of mixed integer programming models. our model can be redu...

This work proposes a novel approach to evaluate and analyze the behavior of multi-population parallel genetic algorithms (PGAs) when running on cluster multi-core processors. In particular, we deeply study their numerical computational by proposing mathematical model representing observed performance curves. them, discuss emerging descriptions PGA instead of, e.g., individual isolated results s...

This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms including parallel island models and parallel cellular genetic algorithms The tutorial also illustrates genetic search by hyperplane sampling The theoretical foundations of genetic algorithms are reviewed include the schema theorem as well as recently developed exact models of the canon...

This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the c...

the objective of this study was to develop a new optimal parallel algorithm for matrix multiplication which could run on a fibonacci hypercube structure. most of the popular algorithms for parallel matrix multiplication can not run on fibonacci hypercube structure, therefore giving a method that can be run on all structures especially fibonacci hypercube structure is necessary for parallel matr...