ABSTRACT
Soft computing (SC) is no longer just a scientific curiosity, but an emerging and refreshingly new computing paradigm that is under continuous development. It has left the confines of computer science and started spreading into the realm of physical, chemical, and other sciences as well. The major components of soft computing include: Genetic algorithms, Evolutionary and genetic computing, Swarm/collective intelligence methods, Artificial neural networks, Simulated annealing, Fuzzy set and fuzzy-logic-based methods. Originally developed by Holland as a computer-executable mathematical model for adaptation in living systems, genetic algorithms (GAs) have proved to be powerful all-purpose problem solvers. Random mutation hill climbing (RMHC) is a single parent evolutionary strategy, mutation being the only genetic operator to act on the parent string. The interfacing of problems of chemistry and physics with SC for obtaining solutions is rather simple once we have chosen a particular soft computing method (SCM) for the problem.
