A searching technique which is based on the biological process of evolution.
Models that optimize rules by mimicking the Darwinian Law of survival of the fittest. A set of rules are chosen by those that work the best. The weakest are discarded. In addition, two successful rules can be combined (the equivalent to genetic cross-overs) to produce offspring rules. The offspring can replace the parents, or they will be discarded if less successful than the parents. Mutation is also accomplished by randomly changing elements. Mutation and cross-over occur with low probability, as in nature.
a model of machine learning that derived its behavior from a metaphor of the processes of evolution in the nature.
Algorithms that mimic the characteristics associated with evolution and that are well suited to optimization problems such as optimizing neural network parameters.
These are routines which are capable of self adaption. As with neural networks, they are based on an analogy with nature; in this case the best algorithms breed with each other to provide new variants in a "survival of the fittest". As yet this cutting edge technology is not widely used in process control.
These are built by a process of evolution, that mimics the development of biological systems. At each stage of evolution the best algorithms are selected for 'breeding'. They are cut and combined with each other to produce the next generation of algorithms. These are then tested against some success criteria to determine which are the best. The process terminates when some threshold of goodness is reached or a time limit expires. Genetic algorithms are useful in many applications where it is difficult to design an algorithm for the task.
numerical optimisation algorithms based on natural selection. They can be applied to a wide variety of problems and offer several key advantages over other, more traditional, techniques ( Genetic Algorithm components)
Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of natural evolution.
A computer-based method of generating and testing combinations of possible input parameters to find the optimal output. It uses processes based on natural evolution concepts such as genetic combination, mutation and natural selection.
