Evolutionary Algorithms

As is the case with ANNs, evolutionary methods take their inspiration from Nature, in this case Darwinism and “survival-of-the-fittest.” Although there are other variants–most notably evolutionary programming and genetic programming–we will restrict our discussion here to that of Genetic Algorithms (GAs). We assume the simplified evolutionary model of Figure 3.

Prior to evolving a solution to the problem of interest, we must first be able to encode potential (candidate) solutions into (fixed-length) genetic string form. As with ANNs, in practice this preprocessing stage can often prove the most difficult part of the exercise. Commencing with random bit strings, we first select two “parent” strings from the available population on the basis of an objective (cost or fitness) function, and proceed to “mate” them. As in biological evolution, a “child” will inherit half of its genetic code (attributes, characteristics) from either parent. The aim is that over time stronger members will “evolve” more appropriate solutions to the problem at hand, while at the same time maintaining sufficient diversity among the population as a whole to ensure healthy future generations. As in nature, a certain degree of randomness (mutation) needs to be injected into this process, in order to prevent “inbreeding” and proceeding too far down evolutionary “blind alleys” (dead ends).

Figure 3. The steps in evolution

Not surprisingly, evolution of an acceptable solution can take a very long time, typically even longer than is the case with ANN training.

Mumford (2008) showed how GAs could be applied to set partitioning problems (such as graph colouring, bin packing and timetabling). Ishibuchi, Nojima and Kowajima (2008) evolved Fuzzy Classifiers using evolutionary techniques. Beale and Pryke (2006) combined GAs with interactive 3D dynamic visualization techniques in the realization of their Haiku Knowledge Discovery system. Tran, Abraham and Jain (2006) combined ANNs, EAs and Fuzzy inference methods in the development of intelligent Decision Support Systems (DSS).

Дата добавления: 2015-07-10