Nonstationary function optimization using genetic algorithm with dominance and diploidy
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Self-adaptation in evolving systems
Artificial Life
Practical Handbook of Genetic Algorithms
Practical Handbook of Genetic Algorithms
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Case-Based Initialization of Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimization
Proceedings of the 6th International Conference on Genetic Algorithms
Adaptation to a Changing Environment by Means of the Feedback Thermodynamical Genetic Algorithm
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Comparison of Dominance Mechanisms and Simple Mutation on Non-stationary Problems
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Adaption to a Changing Environment by Means of the Thermodynamical Genetic Algorithm
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Supporting Polyploidy in Genetic Algorithms Using Dominance Vectors
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Tracking Extrema in Dynamic Environments
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Forking genetic algorithms: Gas with search space division schemes
Evolutionary Computation
Production scheduling and rescheduling with genetic algorithms
Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Towards an analysis of dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Open-ended robust design of analog filters using genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Particle swarm with speciation and adaptation in a dynamic environment
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Univariate marginal distribution algorithms for non-stationary optimization problems
International Journal of Knowledge-based and Intelligent Engineering Systems
Multi-strategy ensemble particle swarm optimization for dynamic optimization
Information Sciences: an International Journal
A self-organized criticality mutation operator for dynamic optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Controlling Particle Trajectories in a Multi-swarm Approach for Dynamic Optimization Problems
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part I: Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy
A Hooke-Jeeves Based Memetic Algorithm for Solving Dynamic Optimisation Problems
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Benchmarking and solving dynamic constrained problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Handling uncertainties in evolutionary multi-objective optimization
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
An analysis of particle properties on a multi-swarm PSO for dynamic optimization problems
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Adaptive evolutionary algorithm based on population dynamics for dynamic environments
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Information Sciences: an International Journal
Adaptive particle swarm optimization algorithm for dynamic environments
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Composite particle optimization with hyper-reflection scheme in dynamic environments
Applied Soft Computing
Multi-swarm co-evolutionary paradigm for dynamic multi-objective optimisation problems
International Journal of Intelligent Information and Database Systems
An orthogonal dynamic evolutionary algorithm with niches
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Evolutionary computing and autonomic computing: shared problems, shared solutions?
Self-star Properties in Complex Information Systems
Performance evaluation of evolutionary heuristics in dynamic environments
Applied Intelligence
An algorithm comparison for dynamic optimization problems
Applied Soft Computing
Solving dynamic constraint optimization problems using ICHEA
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Differential evolution for dynamic environments with unknown numbers of optima
Journal of Global Optimization
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Most research in evolutionary computation focuses on optimization of static, non-changing problems. Many real-world optimization problems, however, are dynamic, and optimization methods are needed that are capable of continuously adapting the solution to a changing environment. If the optimization problem is dynamic, the goal is no longer to find the extrema, but to track their progression through the space as closely as possible. In this chapter, we suggest a classification of dynamic optimization problems, and survey and classify a number of the most widespread techniques that have been published in the literature so far to make evolutionary algorithms suitable for changing optimization problems. After this introduction to the basics, we will discuss in more detail two specific approaches, pointing out their deficiencies and potential. The first approach is based on memorization, the other one uses a novel multi-population structure.