Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A New Interpretation of Schema Notation that Overtums the Binary Encoding Constraint
Proceedings of the 3rd International Conference on Genetic Algorithms
RPL2: A Language and Parallel Framework for Evolutionary Computing
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Evolving Objects: A General Purpose Evolutionary Computation Library
Selected Papers from the 5th European Conference on Artificial Evolution
Selected Papers from AISB Workshop on Evolutionary Computing
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
On parameter tuning in search based software engineering
SSBSE'11 Proceedings of the Third international conference on Search based software engineering
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Evolutionary Computation is an exciting research field with the power to assist researchers in the task of solving hard optimization problems (i.e., problems where the exploitable knowledge about the solution space is very hard and/or expensive to obtain). However, Evolutionary Algorithms are rarely used outside the circle of knowledgeable practitioners, and in that way have not achieved a status of useful enough tool to assist "general" researchers. We think that part of the blame is the lack of practical implementations of research efforts reflecting a unifying common ground in the field. In this communication we present GUIDE, a software framework incorporating some of the latest results from the EC research community and offering a Graphical User Interface that allows the straightforward manipulation of evolutionary algorithms. From a high-level description provided by the user it generates the code that is needed to run an evolutionary algorithm in a specified existing library (as of March 2009, EO and ECJ are the possible targeted libraries). GUIDE's GUI allows users to acquire a straightforward understanding of EC ideas, while at the same time providing them with a sophisticated research tool. In this communication we present~3 industrial case studies using GUIDE as one of the main tools in order to perform software testing on large, complex systems.