A new method is developed for representation and encoding in population-based evolutionary algorithms. The method is inspired by the biological genetic code and utilizes a many-to-...
Abstract. Spatially structured population models improve the performance of genetic algorithms by assisting the selection scheme in maintaining diversity. A significant concern wi...
This paper introduces a novel genetic algorithm strategy based on the reuse of chromosomes from previous generations in the creation of offspring individuals. A number of chromoso...
A novel combination of genetic algorithms and constraint satisfaction modelling for the solution of two and multi-layer over-thecell channel routing problems is presented. The two ...
In this paper we present a genetic algorithm-based approach towards designing self-assembling objects comprised of square smart blocks. Each edge of each block can have one of thr...
Ying Guo, Geoff Poulton, Philip Valencia, Geoff Ja...
The links between genetic algorithms and population-based Markov Chain Monte Carlo (MCMC) methods are explored. Genetic algorithms (GAs) are well-known for their capability to opt...
The use of techniques for automating the generation of software test cases is very important as it can reduce the time and cost of this process. The latest methods for automatic g...
We use a genetic algorithm to calibrate a spatially and temporally resolved cellular automata to model mining activity on public land in Idaho and western Montana. The genetic alg...
Genetic Algorithms provide computational procedures that are modeled on natural genetic system mechanics, whereby a coded solution is “evolved” from a set of potential solutio...
Cancer chemotherapy is a complex treatment mode that requires balancing the benefits of treating tumours using anti-cancer drugs with the adverse toxic side-effects caused by these...
Andrei Petrovski, Bhavani Sudha, John A. W. McCall