Soft Computing

Soft computing is a collection of computational techniques in computer science, machine learning and some engineering regulation, which study, model, and analyze very complex phenomena. . Soft Computing make use of soft techniques contrasting along with classical artificial intelligence hard computing techniques. Rahnamayan (2008) say,For many soft computing methods, we need to generate random numbers to use either as initial estimates or during the learning and search process. Soft computing aids to surmount NP-complete problems by using inexact methods to give useful, but inexact, answers to intractable problems



The principal constituents of Soft Computing (SC) are Fuzzy Logic (FL), Neural Computing (NC), Evolutionary Computation (EC) Machine Learning (ML) and Probabilistic Reasoning (PR ). The important to note for the soft computing is not a mélange. The principal constituent methodologies in SC are complementary rather than competitive. The soft computing is also as a foundation component for the emerging field of conceptual intelligence. Castro (2008) say, the discovery of system usage patterns and is based on a set of soft computing techniques. Also, the framework’s intention is to contribute in alleviating teachers’ workload.



The Importance of Soft Computing is complementarily of FL, NC, GC, and PR , : in many cases a problem can be solved most effectively by using FL, NC, GC and PR in combination rather than exclusively. particularly significant is that in both consumer products and industrial systems, the employment of soft computing techniques leads to systems which have high MIQ (Machine Intelligence Quotient). In large measure, it is the high MIQ of SC-based systems that accounts for the rapid growth in the number and variety of applications of soft computing. McLoughlin (2005) say, Metaheuristic technique that combines Reactive Tabu Search (RE-TS) [1] with evolutionary computing elements that have proven to work well in multimodal search spaces. EETS is a metaheuristic search technique that can be classified as a stochastic method, one of many soft computing techniques . In this paper we describe the background and design of EE-TS and its performance when applied to the Quadratic Assignment Problem (QAP) .



ref :

- Rahnamayan, S., Tizhoosh, H. R., and Salama, M. M. 2008. Opposition versus randomness in soft computing techniques. Appl. Soft Comput. 8, 2 (Mar. 2008), 906-918.

- Castro, F., Nebot, À., and Mugica, F. 2008. A soft computing decision support framework to improve the e-learning experience. In Proceedings of the 2008 Spring Simulation Multiconference (Ottawa, Canada, April 14 - 17, 2008). SpringSim '08. ACM, New York, NY, 781-788.

- McLoughlin, J. F. and Cedeño, W. 2005. The enhanced evolutionary tabu search and its application to the quadratic assignment problem. In Proceedings of the 2005 Conference on Genetic and Evolutionary Computation (Washington DC, USA, June 25 - 29, 2005). H. Beyer, Ed. GECCO '05. ACM, New York, NY, 975-982.