Achieving memetic adaptability by means of fuzzy decision trees

Evolutionary Algorithms are a collection of optimization techniques that take their inspiration from natural selection and survival of the fittest in the biological world and they have been exploited to try to resolve some of the more complex NP-complete problems. Nevertheless, in spite of their capability of exploring and exploiting promising regions of the search space, they present some drawbacks and, in detail, they can take a relatively long time to locate the exact optimum in a region of convergence and may sometimes not find the solutions with sufficient precision. Memetic Algorithms are innovative meta-heuristic search methods that try to alleviate evolutionary approaches' weaknesses by efficiently converging to high quality solutions. However, as shown in literature, memetic approaches are affected by several design issues related to the different choices that can be made to implement them. This paper introduces a multi-agent based memetic algorithm which executes in a parallel way different cooperating optimization strategies in order to solve a given problem's instance in an efficient way. The algorithm adaptation is performed by jointly exploiting a knowledge extraction process, based on fuzzy decision trees, together with a decision making framework based on fuzzy methodologies. The effectiveness of our approach is tested in several experiments in which our results are compared with those obtained by some non-adaptive memetic algorithms.

[1]  Andries Petrus Engelbrecht,et al.  CIlib: A collaborative framework for Computational Intelligence algorithms - Part I , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[2]  Francisco Herrera,et al.  A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability , 2009, Soft Comput..

[3]  J. Krarup,et al.  The simple plant location problem: Survey and synthesis , 1983 .

[4]  Pedro S. de Souza,et al.  Asynchronous Teams: Cooperation Schemes for Autonomous Agents , 1998, J. Heuristics.

[5]  Edmund K. Burke,et al.  Multimeme Algorithms for Protein Structure Prediction , 2002, PPSN.

[6]  Andy J. Keane,et al.  Meta-Lamarckian learning in memetic algorithms , 2004, IEEE Transactions on Evolutionary Computation.

[7]  Francisco Herrera,et al.  Ten years of genetic fuzzy systems: current framework and new trends , 2004, Fuzzy Sets Syst..

[8]  Jim Smith,et al.  Co-evolving Memetic Algorithms: Initial Investigations , 2002, PPSN.

[9]  Pierre Hansen,et al.  Cooperative Parallel Variable Neighborhood Search for the p-Median , 2004, J. Heuristics.

[10]  M. Carmen Garrido,et al.  Using machine learning in a cooperative hybrid parallel strategy of metaheuristics , 2009, Inf. Sci..

[11]  Andries Petrus Engelbrecht,et al.  CIlib: A collaborative framework for Computational Intelligence algorithms - Part II , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[12]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[13]  Cezary Z. Janikow,et al.  Fuzzy decision trees: issues and methods , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[14]  Daijin Kim,et al.  An optimal design of neuro-FLC by Lamarckian co-adaptation of learning and evolution , 2001, Fuzzy Sets Syst..

[15]  Giovanni Acampora,et al.  Fuzzy control interoperability and scalability for adaptive domotic framework , 2005, IEEE Transactions on Industrial Informatics.

[16]  Pierluigi Ritrovato,et al.  Optimizing learning path selection through memetic algorithms , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[17]  James Smith,et al.  A tutorial for competent memetic algorithms: model, taxonomy, and design issues , 2005, IEEE Transactions on Evolutionary Computation.