Design patterns: elements of reusable object-oriented software
Design patterns: elements of reusable object-oriented software
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Evolving Objects: A General Purpose Evolutionary Computation Library
Selected Papers from the 5th European Conference on Artificial Evolution
The Journal of Machine Learning Research
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
ParadisEO-MOEO: a framework for evolutionary multi-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
The EvA2 optimization framework
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Opt4J: a modular framework for meta-heuristic optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
jMetal: A Java framework for multi-objective optimization
Advances in Engineering Software
IEEE Transactions on Visualization and Computer Graphics
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A review of multiobjective test problems and a scalable test problem toolkit
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm
IEEE Transactions on Evolutionary Computation
Quo vadis, evolutionary computation?: on a growing gap between theory and practice
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
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Although there exists a number of optimization frameworks only commercial and closed source software address, to an extent, real-world optimization problems and arguably these software packages are not very easy to use. In this work we introduce an open source integrated optimization environment which is designed to be extensible and have a smooth learning curve so that it can be used by the non-expert in industry. We call this environment, Liger. Liger is an application that is built about a visual programming language, by which optimization work-flows can be created. Additionally, Liger provides a communication layer with external tools, whose functionality can be directly integrated and used with native components. This fosters code reuse and further reduces the required effort on behalf of the practitioner in order to obtain a solution to the optimization problem. Furthermore, there exists a number of available algorithms which are fully configurable, however should the need arise new algorithms can also be created just as easily by reusing what we call operator nodes. Operator nodes perform specific tasks on a set, or a single solution. Lastly as visual exploration of the obtained solutions is essential for decision makers, we also provide state-of-the art visualization capabilities.