Evolution optimization, evolutionary

The desire to create RNA molecules with predefined properties and to optimize their efficiencies and specificities has led to a new technique called evolutionary biotechnology or applied molecular evolution. Natural selection or its analogue in test-tube evolution optimizes fitness or replication rate constants, respectively. High replication rates, however, are neither required nor wanted in the search for... 

The term evolutionary algorithm (EA) refers to a class of population based metaheuristic (probabilistic) optimization algorithms which imitate the Darwinian evolution ( survival ofthe fittest ). However, the biological terms are used as metaphors rather than in their exact meaning. The population of individuals denotes a set of solution candidates or points of the solution space. Each individual represents a point in the search space which is coded in the individual s representation (genome). The fitness of an individual is usually defined on the basis of the value of the objective function and determines its chances to stay in the population and to be used to generate new solution points. 

Recently, we proposed the Multi-objective Evolutionary Graph Algorithm (MEGA), an optimization algorithm designed for the evolution of chemical structures satisfying multiple constraints... 

The model of evolutionary dynamics has been applied to interpret the experimental data on molecular evolution and it was implemented for computer simulations (Huynen et al., 1996 Fontana and Schuster, 1998). The computer simulations allows one to follow the optimization process in full detail at the molecular level. 

Professor Kula spent six month on sabbatical leave at the California Institute of Technology with Professor Frances H. Arnold, who has contributed a paper on the topic of Optimizing Industrial Enzymes by Directed Evolution . Professor Arnold shows how enzymes usable in practice, for which in nature under natural conditions there has been no evolutionary stress, can result from directed evolution. 

Evolutionary computation approaches are optimization methods. They are conveniently presented using the metaphor of natural evolution a randomly initialized population of individuals evolves following a crude parody of the Darwinian principle of the survival of the fittest. New individuals are generated using simulated evolutionary operations such as mutations. The probability of survival of the newly generated solutions depends on their fitness (how well they perform with respect to the optimization problem at hand) the best are kept with a high probability, the worst are rapidly discarded. 

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