Introduction to Genetic Algorithms
Springer Science & Business Media, Oct 24, 2007 - Technology & Engineering - 442 pages
Theoriginofevolutionaryalgorithmswasanattempttomimicsomeoftheprocesses taking place in natural evolution. Although the details of biological evolution are not completely understood (even nowadays), there exist some points supported by strong experimental evidence: • Evolution is a process operating over chromosomes rather than over organisms. The former are organic tools encoding the structure of a living being, i.e., a cr- ture is “built” decoding a set of chromosomes. • Natural selection is the mechanism that relates chromosomes with the ef ciency of the entity they represent, thus allowing that ef cient organism which is we- adapted to the environment to reproduce more often than those which are not. • The evolutionary process takes place during the reproduction stage. There exists a large number of reproductive mechanisms in Nature. Most common ones are mutation (that causes the chromosomes of offspring to be different to those of the parents) and recombination (that combines the chromosomes of the parents to produce the offspring). Based upon the features above, the three mentioned models of evolutionary c- puting were independently (and almost simultaneously) developed.
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adaptive allows ants applied approach assigned attributes better binary blocks building called cell chromosome combination combinatorial optimization components considered constraints contain convergence cost create crossover defined determine developed dominance edge encoding evaluation evolution evolutionary example fitness function fitness value follows fuzzy gene genetic algorithm genetic programming given implemented important improve increase individuals initial input iteration knowledge learning length machine mating method minimize mutation natural nodes objective function obtained offspring operators optimization problems output parallel parameters parents performance population position possible probability produce random randomly reliability representation represents requires Research roulette wheel selection rules scheduling scheme search space selection shown shows similar simple single solution solve specific Step string structure techniques terminal tion tree variables various
Page 405 - This value is called pbest. Another 'best ' value that is tracked by the global version of the particle swarm optimizer is the overall best value, and its location, obtained so far by any particle in the population. This location is called gbest.
Page 135 - There must be what the patent law refers to as an "illogical step" (ie, an unjustified step) to distinguish a putative invention from that which is readily deducible from that which is already known. Humans supply the critical ingredient of "illogic" to the invention process. Interestingly, everyday usage parallels the patent law concerning inventiveness: people who mechanically apply existing facts in well-known ways are summarily dismissed as being uncreative. Logical thinking is unquestionably...
Page 153 - The result is equal to or better than a result that was accepted as a new scientific result at the time when it was published in a peer-reviewed scientific journal.
Page 342 - ... AQ15 produce sets of classification rules that are not necessarily optimal with respect to (1) the need to minimize the number of features actually used for classification and (2) the need to achieve high recognition rates with noisy data. This chapter describes one of several multistrategy approaches being explored to improve the usefulness of machine learning techniques for such problems. The approach described here involves the use of genetic algorithms as a "front end...
Page 403 - Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling.
Page 403 - The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation.
Page 153 - ... journal. C. The result is equal to or better than a result that was placed into a database or archive of results maintained by an internationally recognized panel of scientific experts. D. The result is publishable in its own right as a new scientific result (independent of the fact that the result was mechanically created). E. The result is equal to or better than the most recent human-created solution to a long-standing problem for which there has been a succession of increasingly better human-created...
Page 417 - AGO meta-heuristic, can be used to implement centralized actions which cannot be performed by single ants. Examples are the activation of a local optimization procedure, or the collection of global information that can be used to decide whether it is useful or not to deposit additional pheromone to bias the search process from a non-local perspective. As a practical example, the daemon...
Page 152 - High-Return. What is delivered by the actual automated operation of an artificial method in comparison to the amount of knowledge, information, analysis, and intelligence that is pre-supplied by the human employing the method? We define the AI ratio (the "artificial-to-intelligence" ratio) of a problem-solving method as the ratio of that which is delivered by the automated operation of the artificial method to the amount of intelligence that is supplied by the human applying the method to a particular...
Page 143 - ... programming require the human user to specify (1) the set of terminals (eg, the independent variables of the problem, zeroargument functions, and random constants) for each branch of the to-be-evolved...