Abstract
Many applications of optimization techniques, such as classification and regression problems, require long simulations to evaluate the performance of their solutions. Problems where the fitness function can be divided into smaller pieces—problem partitioning—demand techniques that approximate the overall fitness from that obtained in a small region of the problem space. This means that less time is spent evaluating individual solutions, which makes such approaches computationally efficient.
In this work, a method is proposed to deal with a dynamically calculated fitness function; it is called Genetic Algorithm with Base Fitness (GABF). This method is built over a Genetic Algorithm (GA) to optimize a Fuzzy Rule-Based System (FRBS). The proposed method works by partitioning training data into smaller subsets. The main idea is to assign fitness values derived from part of the training set (or a short simulation) to individuals in the current generation. This fitness value is then inherited and combined with those obtained in subsequent generations.
To test the proposal, a scenario in which two vehicles are approaching an intersection is implemented. One vehicle is presumed to be driven by a human and does not change its speed, whereas the other implements an autonomous speed regulator based on fuzzy logic. The regulator must maneuver the autonomous vehicle in a safe and efficient manner. The objective is to optimize both the membership functions and the rule base of the fuzzy system controlling the autonomous vehicle.
Similar content being viewed by others
References
Alcalá R, Gacto MJ, Herrera F (2011) A fast and scalable multiobjective genetic fuzzy system for linguistic fuzzy modeling in high-dimensional regression problems. IEEE Trans Fuzzy Syst 19(4):666–681
Brindle A (1981) Genetic algorithms for function optimization. PhD thesis, University of Alberta
Bui L, Abbass H, Essam D (2005) Fitness inheritance for noisy evolutionary multi-objective optimization. In: Proceedings of the conference on genetic and evolutionary computation, pp 779–785
Cano JR, Herrera F, Lozano M (2006) On the combination of evolutionary algorithms and stratified strategies for training set selection in data mining. Appl Soft Comput 6(3):323–332
Cano JR, Herrera F, Lozano M (2007) Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability. Data Knowl Eng 60(1):90–108
Cantu-Paz E (2000) Efficient and accurate parallel genetic algorithms, vol 1. Kluger Academic, Boston
Cantú-Paz E, Goldberg D (1999) On the scalability of parallel genetic algorithms. Evol Comput 7(4):429–449
Eshelman L, Schaffer J (1993) Real coded genetic algorithms and interval schemata. Foundation of genetic algorithms, vol 2. Morgan Kaufmann, San Mateo
Ferri C, Hernández-Orallo J, Modroiu R (2009) An experimental comparison of performance measures for classification. Pattern Recognit Lett 30(1):27–38
Fonseca L, Lemonge A, Barbosa H (2012) A study on fitness inheritance for enhanced efficiency in real-coded genetic algorithms. In: IEEE congress on evolutionary computation, pp 1–8
Gacto MJ, Alcalá R, Herrera F (2012) A multi-objective evolutionary algorithm for an effective tuning of fuzzy logic controllers in heating, ventilating and air conditioning systems. Appl Intell 36(2):330–347
Ghosh Nee De S, Ghosh A, Pal S (2003) Incorporating ancestors’ influence in genetic algorithms. Appl Intell 18(1):7–25
Goldberg D (1989) Genetic algorithms in optimization, search and machine learning. Addison Wesley, Reading, MA
Goldberg D (1998) The race, the hurdle, and the sweet spot: lessons from genetic algorithms for the automation of design innovation and creativity. Tech Rep 98007, University of Illinois at Urbana-Champaign
Goldberg D, Deb K (1991) A comparative analysis of selection schemes used in genetic algorithms. Morgan Kaufmann, San Mateo
Gu J, Gu M, Cao C, Gu X (2010) A novel competitive co-evolutionary quantum genetic algorithm for stochastic job shop scheduling problem. Comput Oper Res 37(5):927–937
Hart W, Krasnogor N, Smith J (2004) Recent advances in memetic algorithms. Studies in fuzzyness and soft computing series, vol 166. Springer, Berlin
Herrera F (2008) Genetic fuzzy systems: taxonomy, current research trends and prospects. Evol Intell 1:27–46
Holland J (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor
Jin Y (2005) A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput 9(1):3–12
Kaya M, Alhajj R (2006) Utilizing genetic algorithms to optimize membership functions for fuzzy weighted association rules mining. Appl Intell 24(1):7–15
Liu H, Motoda H (2002) On issues of instance selection. Data Min Knowl Discov 6(2):115–130
Luong H, Nguyen H, Ahn C (2012) Entropy-based efficiency enhancement techniques for evolutionary algorithms. Inf Sci 188:100–120
Moscato P, Cotta C, Mendes A (2004) Memetic algorithms. In: New optimization techniques in engineering. Studies in fuzziness and soft computing, vol 141. Springer, Berlin, pp 53–85
Onieva E, Naranjo J, Milanés V, Alonso J, García R, Pérez J (2011) Automatic lateral control for unmanned vehicles via genetic algorithms. Appl Soft Comput 11(1):1303–1309
Onieva E, Milanés V, Villagrá J, Pérez J, Godoy J (2012) Genetic optimization of a vehicle fuzzy decision system for intersections. Expert Syst Appl 39(18):13,148–13,157
Panoutsos G, Mahfouf M (2010) A neural-fuzzy modelling framework based on granular computing: concepts and applications. Fuzzy Sets Syst 161(21):2808–2830
Paredis J (2000) Coevolutionary algorithms. Evol Comput 2:224–238
Precup RE, Hellendoorn H (2011) A survey on industrial applications of fuzzy control. Comput Ind 62(3):213–226
Sastry K (2001) Evaluation-relaxation schemes for genetic and evolutionary algorithms. PhD thesis, University of Illinois at Urbana-Champaign
Sastry K, Goldberg DE (2004) Designing competent mutation operators via probabilistic model building of neighborhoods. In: Deb K (ed) Genetic and evolutionary computation—GECCO 2004. Lecture notes in computer science, vol 3103. Springer, Berlin, pp 114–125
Sastry K, Goldberg DE (2004) Let’s get ready to rumble: crossover versus mutation head to head. In: Deb K (ed) Genetic and evolutionary computation—GECCO 2004. Lecture notes in computer science, vol 3103. Springer, Berlin, pp 126–137
Smith SF (1980) A learning system based on genetic adaptive algorithms. PhD thesis, University of Pittsburgh
Sugeno M (1999) On stability of fuzzy systems expressed by fuzzy rules with singleton consequents. IEEE Trans Fuzzy Syst 7(2):201–224
Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15(1):116–132
Tejado I, Milanés V, Villagrá J, Godoy J, HosseinNia H, Vinagre B (2011) Low speed control of an autonomous vehicle by using a fractional PI controller. In: IFAC world congress, vol 18, pp 15025–15030
Yaochu J, Branke J (2005) Evolutionary optimization in uncertain environments—a survey. IEEE Trans Evol Comput 9(3):303–317
Zadeh L (1965) Fuzzy sets. Inf Control 8(3):338–353
Acknowledgements
The authors would like to thank the EU Intelligent Cooperative Sensing for Improved Traffic Efficiency (ICSI) project (FP7-ICT-2011-8) for its support in the development of this work.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Onieva, E., Osaba, E., Zhang, X. et al. GABF: genetic algorithm with base fitness for obtaining generality from partial results: study in autonomous intersection by fuzzy logic. Appl Intell 41, 1–12 (2014). https://doi.org/10.1007/s10489-013-0498-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-013-0498-5