Our empirical outcomes reveal the effectiveness of using the genetic algorithm in obtaining the product configurations that meet the best level of trading-off between steps and decisions at a ...

In contrast, evolutionary algorithms solves a problem based on criteria set in the “fitness” function—thus requiring little to no data.

When solving a multi-objective optimization problem using Evolutionary Algorithms, the diversity loss can occur as the evolution process is made. This is particularly significant in Pareto-based ...

In the 1980s and 1990s, Schmidhuber and others explored evolutionary algorithms for improving coding agents, creating programs that write programs. An evolutionary algorithm takes something (such as a ...

Evolutionary algorithm outperforms deep-learning machines at video games Neural networks have garnered all the headlines, but a much more powerful approach is waiting in the wings.

Simple Numerical Optimization Using an Evolutionary Algorithm with C# Dr. James McCaffrey of Microsoft Research says that when quantum computing becomes generally available, evolutionary algorithms ...

Evolutionary algorithms represent a class of optimisation techniques inspired by the principles of biological evolution. These methods iteratively modify candidate solutions using operations ...

This repository contains the code and research papers of two group projects (tasks) that are part of the Master's course Evolutionary Computing (course description) at the Vrije Universiteit Amsterdam ...

Stephanie Ness, also called Mrs Ness is an international, certified and influential specialist on evolutionary algorithms. She studied investment and information technology at Harvard University ...