Michał W. Przewoźniczek

Latest

Cloud-based dynamic distributed optimisation of integrated process planning and scheduling in smart factories
Parameter-less population pyramid with automatic feedback
Parameter-less, population-sizing DSMGA-II
The transformation of the k-Shortest Steiner Trees search problem into binary dynamic problem for effective evolutionary methods application
Universal strategy of dynamic subpopulation number management in practical network optimization problems
The influence of fitness caching on modern evolutionary methods and fair computation load measurement
The practical use of problem encoding allowing cheap fitness computation of mutated individuals
Parameter-less population pyramid with feedback
Problem encoding allowing cheap fitness computation of mutated individuals
The effectiveness of the simplicity in evolutionary computation
The evolutionary cost of Baldwin effect in the routing and spectrum allocation problem in elastic optical networks
Active multi-population pattern searching algorithm for flow optimization in computer networks – the novel coevolution schema combined with linkage learning
Dynamic subpopulation number control for solving routing and spectrum allocation problems in elastic optical networks
Linked genes migration in Island Models
Constructive heuristics for technology-driven resource constrained scheduling problem
Multi population pattern searching algorithm for solving routing spectrum allocation with joint unicast and anycast problem in elastic optical networks
Towards finding an effective uniform and single point crossover balance for optimization of Elastic Optical Networks
Towards finding an effective way of discrete problems solving: the particle swarm optimization, genetic algorithm and linkage learning techniques hybrydization
Towards solving practical problems of large solution space using a novel pattern searching hybrid evolutionary algorithm – an elastic optical network optimization case study
Multi population pattern searching algorithm :a new evolutionary method based on the idea of messy genetic algorithm