A pseudo genetic algorithm

Abstract

Random individual initialization tends to generate too many eccentric and homogeneous individuals which cause slow and premature convergence. It needs many operations (selection strategy, incest prevention and mutation) to fix, which consume too much computation and lose many good genes. The proposed complementary-parent strategy initializes every other pair of parents with dynamically or statically complementary chromosomes (such as 010101…0101 and 101010…1010). Crossover of every generation is only performed between the offspring from the same parents, during which the parents are completely replaced by their own children. Higher population diversity is got without gene lost at all, by which search ability is enhanced. Incest prevention, selection strategies and mutation are unnecessary and consequently cancelled (so it is named pseudo genetic algorithm). As indicated by the simulation results, the speed of elitist search is accelerated greatly and computation complexity is reduced by half.

A Dual Heterogeneous Island Genetic Algorithm for Solving Large Size Flexible Flow Shop Scheduling Problems on Hybrid Multicore CPU and GPU Platforms

“…The population size is kept as 512 for all tested GAs while the subpopulation size for each island of heterogeneous GA is 256. The crossover rate and the mutation rate of cellular GA are set as 1.00 and 0.05 respectively [23], while the crossover rate of pseudo GA is equal to 0.75 [24]. The cellular GA from the dual heterogeneous GA keeps the same crossover rate and mutation rate as the cellular GA.…”

Localized Island Model Genetic Algorithm in Population Diversity Preservation

“…Moreover in previous researches [19], [20], LIMGA used three different algorithms as each island's GA core. This research applied single machine model such as Standard GA (SGA) [21] for balance, Pseudo GA (PGA) [22] for speed, and Informed GA (IGA) [7] for performance. Figure 2 shows illustration of LIMGA mechanism used in this work.…”

“…By considering it, pseudo GA (PGA) and informed GA (IGA) were chosen. For computationally expensive optimization, PGA [22] is still implementable because of its flexibility. Contrary, IGA is a variant which depends on the case's prior knowledge.…”

“…2) Pseudo Genetic Algorithm: PGA has a quite similar process with SGA, but it implements no roulette wheel selection and mutation. PGA uses complementary chromosome [22] which is static-dynamic for initialization and crossover instead. This mechanism avoids PGA from incest breeding by creating the complement of a parent chromosome to be its couple.…”

Energy-Efficient Routing Control Algorithm in Large-Scale WSN for Water Environment Monitoring with Application to Three Gorges Reservoir Area

“…Designing an energy-efficient routing algorithm is the best way to prolong network lifetime of WSN and improve the quality of information transmission [ 4 , 5 ]. Currently, a variety of routing protocols have been proposed to save energy.…”