English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Traditionally Genetic Algorithm has been used for optimization of unimodaland multimodal functions. Earlier researchers worked with constantprobabilities of GA control operators like crossover, mutation etc. for tuningthe optimization in specific domains. Recent advancements in this fieldwitnessed adaptive approach in probability determination. In Adaptive mutationprimarily poor individuals are utilized to explore state space, so mutationprobability is usually generated proportionally to the difference betweenfitness of best chromosome and itself (fMAX - f). However, this approach issusceptible to nature of fitness distribution during optimization. This paperpresents an alternate approach of mutation probability generation usingchromosome rank to avoid any susceptibility to fitness distribution.Experiments are done to compare results of simple genetic algorithm (SGA) withconstant mutation probability and adaptive approaches within a limited resourceconstraint for unimodal, multimodal functions and Travelling Salesman Problem(TSP). Measurements are done for average best fitness, number of generationsevolved and percentage of global optimum achievements out of several trials.The results demonstrate that the rank-based adaptive mutation approach issuperior to fitness-based adaptive approach as well as SGA in a multimodalproblem space.

A Rank based Adaptive Mutation in Genetic Algorithm