GREAT IDEAs

Some researcher said:

• "Evolutionary methods are most often used on hard optimization problems rather than pattern recognition"
• Some decades ago, people thought that genetic and evolutionary algorithms were swiss-army-knives, fueled by spectacular early results. Statements like the building block hypothesis were made in an effort to prove that they were in general good strategies. 
• Most prominently the No Free Lunch Theorem. 
• It became evident that genetic/evolutionary algorithms are often decent heuristics but never optimal in any sense.
• Lets research more on No Free Lunch Theorem....(will continue)

Building block hypothesis (BBH)

Genetic algorithms are simple to implement, but their behavior is difficult to understand. In particular it is difficult to understand why these algorithms frequently succeed at generating solutions of high fitness when applied to practical problems. The building block hypothesis (BBH) consists of:

1. A description of a heuristic that performs adaptation by identifying and recombining "building blocks", i.e. low order, low defining-length schemata with above average fitness.
2. A hypothesis that a genetic algorithm performs adaptation by implicitly and efficiently implementing this heuristic.

Goldberg describes the heuristic as follows:

"Short, low order, and highly fit schemata are sampled, recombined [crossed over], and resampled to form strings of potentially higher fitness. In a way, by working with these particular schemata [the building blocks], we have reduced the complexity of our problem; instead of building high-performance strings by trying every conceivable combination, we construct better and better strings from the best partial solutions of past samplings.

"Because highly fit schemata of low defining length and low order play such an important role in the action of genetic algorithms, we have already given them a special name: building blocks. Just as a child creates magnificent fortresses through the arrangement of simple blocks of wood, so does a genetic algorithm seek near optimal performance through the juxtaposition of short, low-order, high-performance schemata, or building blocks."