Summary
In this chapter we propose a new evolutionary elitist approach combining a non-standard solution representation and an evolutionary optimization technique. The proposed method permits detection of continuous decision regions. In our approach an individual (a solution) is either a closed interval or a point. The individuals in the final population give a realistic representation of the Pareto-optimal set. Each solution in this population corresponds to a decision region of the Pareto-optimal set. The proposed technique is an elitist one. It uses a unique population. The current population contains non-dominated solutions already computed.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Corne, DW and Knowles, JD., The Pareto-Envelope Based Selection Algorithm for Multiobjective Optimization. In Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature, Springer-Verlag, pp. 839–848, Berlin, 2000
Deb, K, (1999) Multiobjective Evolutionary Algorithms: Problem Difficulties and Construction of Test Problems. Evolutionary Computation 1999; 7: 205–230.
Deb, K, Agrawal, S, Pratap, A and Meyarivan, T, A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA II. In Schoenauer, M, et al. (eds), Parallel Problem Solving From Nature-PPSN VI, Springer-Verlag, pp. 849–858, Berlin, 2000.
Dumitrescu, D, Grosan, C and Oltean M, An Evolutionary Algorithm for Detecting Continuous Pareto Regions. Studia Babes-Bolyai University, Ser. Informatica, 2000; 45: 51–68.
Goldberg, DE, Evolutionary Algorithms in Search, Optimization and Machine Learning. Reading, Addison Wesley, 1989.
Grosan, C, A New Evolutionary Technique for Detecting Pareto Continuos Regions, Proceedings of Genetic and Evolutionary Computation Conference (GECCO-2003), Workshop Program, Barry, A (ed), pp. 304–307, 2003.
Horn, J and Nafpliotis, N, Multiobjective Optimization Using Niched Pareto Evolutionary Algorithms. IlliGAL Report 93005, Illinois Evolutionary Algorithms Laboratory, University of Illinois, Urbana Champaign, 1993.
Horn, J, Nafpliotis, N and Goldberg, DE, A Niche Pareto Evolutionary Algorithm for Multiobjective Optimization. In Proc. 1st IEEE Conf. Evolutionary Computation, Piscataway, 1: pp. 82–87, 1994.
Knowles, JD and Corne, DW, Approximating the Nondominated Front Using the Pareto Archived Evolution Strategies. Evolutionary Computation 2000; 8(2):149–172.
Knowles, JD and Corne, DW, The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Pareto Multiobjective Optimization. In Congress on Evolutionary Computation (CEC 99), Piscataway, NJ, 1: pp. 98–105, 1999.
Schaffer, JD, Multiple Objective Optimization with Vector Evaluated Evolutionary Algorithms. Evolutionary Algorithms and Their Applications, In Grefenstette, JJ, (ed.), pp. 93–100, Hillsdale, NJ, Erlbaum, 1985.
Srinivas, N and Deb, K, Multiobjective Function Optimization Using Nondominated Sorting Evolutionary Algorithms. Evolutionary Computing 1994; 2: 221–248.
Zitzler, E, Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. Doctoral Dissertation, Swiss Federal Institute of Technology Zurich, 1999.
Zitzler, E, Laumanns, M and Thiele, L, SPEA 2: Improving the Strength Pareto Evolutionary Algorithm, TIK Report 103, Computer Engineering and Networks Laboratory (TIK), Department of Electrical Engineering, Swiss Federal Institute of Technology (ETH) Zurich, 2001.
Coello, CAC, A Comprehensive Survey of Evolutionary-based Multiobjective Optimization Techniques. Knowledge and Information Systems, 1999; 1(3): 269–308.
Veldhuizen, DAV, Multiobjective Evolutionary Algorithms: Classification, Analyses and New Innovations. Ph.D Thesis, Graduated School of Engineering of the Air Force Institute of Technology, Air University, 1999.
Veldhuizen, DAV and Lamont, GB, Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-art. Evolutionary Computation, 2000; 8: 125–147.
Dumitrescu, D, Genetic Chromodynamics. Studia, Babes-Bolyai University, Ser. Informatica, 2000; 45: 39–50.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag London Limited
About this chapter
Cite this chapter
Dumitrescu, D., Groşan, C., Oltean, M. (2005). Evolving Continuous Pareto Regions. In: Abraham, A., Jain, L., Goldberg, R. (eds) Evolutionary Multiobjective Optimization. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/1-84628-137-7_8
Download citation
DOI: https://doi.org/10.1007/1-84628-137-7_8
Publisher Name: Springer, London
Print ISBN: 978-1-85233-787-2
Online ISBN: 978-1-84628-137-2
eBook Packages: Computer ScienceComputer Science (R0)