Soft Computing - Introduction

Introduction

Soft Computing became a formal Computer Science area of study in early 1990s. Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising in biology, medicine, the humanities, management sciences, and similar fields often remained intractable to conventional mathematical and analytical methods. That said, it should be pointed out that simplicity and complexity of systems are relative, and many conventional mathematical models have been both challenging and very productive. Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve practicability, robustness and low solution cost. Components of soft computing include:

• Neural networks (NN)
• Fuzzy logics (FL)
• Evolutionary computation (EC), including:
  • Evolutionary algorithms
    • Genetic algorithms
    • Differential evolution
  • Metaheuristic and Swarm Intelligence
    • Ant colony optimization
    • Bees algorithms
    • Bat algorithm
    • Cuckoo search
    • Harmony search
    • Firefly algorithm
    • Artificial immune systems
    • Particle swarm optimization

• Ideas about probability including:
  • Bayesian network
• Chaos theory
• Perceptron

Generally speaking, soft computing techniques resemble biological processes more closely than traditional techniques, which are largely based on formal logical systems, such as sentential logic and predicate logic, or rely heavily on computer-aided numerical analysis (as in finite element analysis). Soft computing techniques are intended to complement each other.

Unlike hard computing schemes, which strive for exactness and full truth, soft computing techniques exploit the given tolerance of imprecision, partial truth, and uncertainty for a particular problem. Another common contrast comes from the observation that inductive reasoning plays a larger role in soft computing than in hard computing.