Introduction

EvA2 (an Evolutionary Algorithms framework, revised version 2) is a comprehensive heuristic optimization framework with emphasis on Evolutionary Algorithms implemented in Java. It is a revised version of the JavaEvA optimization toolbox, which has been developed as a resumption of the former EvA software package.

EvA2 integrates several derivation free optimization methods, preferably population based, such as Evolution Strategies (ES), Genetic Algorithms (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), as well as classical techniques such as multi-start Hill Climbing or Simulated Annealing. Besides typical single-objective problems, multi-modal and multi-objective problem are handled directly by the EvA2 framework. Via the Java mechanism of Remote Method Invocation (RMI), the algorithms of EvA2 can be distributed over network nodes based on a client-server architecture.

EvA2 aims at two groups of users. Firstly, the end user who does not know much about the theory of Evolutionary Algorithms, but wants to use Evolutionary Algorithms to solve an application problem. Secondly, the scientific user who wants to investigate the performance of different optimization algorithms or wants to compare the effect of alternative or specialized evolutionary or heuristic operators. The latter usually knows more about evolutionary algorithms or heuristic optimization and is able to extend EvA2 by adding specific optimization strategies or solution representations.

EvA2 is being used as teaching aid in lecture tutorials, as a developing platform in student research projects and applied to numerous optimisation problems within active research and ongoing industrial cooperations.