Genetic programming II (videotape): the next generation
Genetic programming II (videotape): the next generation
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Search space division in GAs using phenotypic properties
Information Sciences: an International Journal
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Genetic Programming and Evolvable Machines
Automatic Creation of Human-Competitive Programs and Controllers by Means of Genetic Programming
Genetic Programming and Evolvable Machines
A Scalable Approach to Evolvable Hardware
Genetic Programming and Evolvable Machines
Evolving neural networks through augmenting topologies
Evolutionary Computation
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
Sizing Populations for Serial and Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Programming And Multi-agent Layered Learning By Reinforcements
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Adaptive Hierarchical Fair Competition (AHFC) Model For Parallel Evolutionary Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Scalability Problems of Digital Circuit Evolution: Evolvability and Efficient Designs
EH '00 Proceedings of the 2nd NASA/DoD workshop on Evolvable Hardware
Optimal design of flywheels using an injection island genetic algorithm
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions
Evolutionary Computation
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
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
Expert Systems with Applications: An International Journal
A synergistic approach for evolutionary optimization
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Automated synthesis of mechanical vibration absorbers using genetic programming
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Parameter optimization for growth model of greenhouse crop using genetic algorithms
Applied Soft Computing
An Evolutionary ILS-Perturbation Technique
HM '08 Proceedings of the 5th International Workshop on Hybrid Metaheuristics
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
How novelty search escapes the deceptive trap of learning to learn
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Computers and Operations Research
Cooperation in the context of sustainable search
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Ensemble of niching algorithms
Information Sciences: an International Journal
Efficient protein-ligand docking using sustainable evolutionary algorithm
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Abstract functions and lifetime learning in genetic programming for symbolic regression
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Open issues in genetic programming
Genetic Programming and Evolvable Machines
Clustering-based hierarchical genetic algorithm for complex fitness landscapes
International Journal of Intelligent Systems Technologies and Applications
Evolving plastic neural networks with novelty search
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
Evolving a diversity of virtual creatures through novelty search and local competition
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Advances in Engineering Software
Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms
International Journal of Intelligent Information Technologies
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Many current Evolutionary Algorithms (EAs) suffer from a tendency to converge prematurely or stagnate without progress for complex problems. This may be due to the loss of or failure to discover certain valuable genetic material or the loss of the capability to discover new genetic material before convergence has limited the algorithm's ability to search widely. In this paper, the Hierarchical Fair Competition (HFC) model, including several variants, is proposed as a generic framework for sustainable evolutionary search by transforming the convergent nature of the current EA framework into a non-convergent search process. That is, the structure of HFC does not allow the convergence of the population to the vicinity of any set of optimal or locally optimal solutions. The sustainable search capability of HFC is achieved by ensuring a continuous supply and the incorporation of genetic material in a hierarchical manner, and by culturing and maintaining, but continually renewing, populations of individuals of intermediate fitness levels. HFC employs an assembly-line structure in which subpopulations are hierarchically organized into different fitness levels, reducing the selection pressure within each subpopulation while maintaining the global selection pressure to help ensure the exploitation of the good genetic material found. Three EAs based on the HFC principle are tested - two on the even-10-parity genetic programming benchmark problem and a real-world analog circuit synthesis problem, and another on the HIFF genetic algorithm (GA) benchmark problem. The significant gain in robustness, scalability and efficiency by HFC, with little additional computing effort, and its tolerance of small population sizes, demonstrates its effectiveness on these problems and shows promise of its potential for improving other existing EAs for difficult problems. A paradigm shift from that of most EAs is proposed: rather than trying to escape from local optima or delay convergence at a local optimum, HFC allows the emergence of new optima continually in a bottom-up manner, maintaining low local selection pressure at all fitness levels, while fostering exploitation of high-fitness individuals through promotion to higher levels.