ICS 691: Genetic Programming Project Proposal Analysis of the Genealogical Impacts of Memetic Crossover
نویسنده
چکیده
In problem domains where the evaluation of the individual significantly dominates the rest of the evolutionary process with respect to time, such as robotic control, the viability of an evolutionary approach can be called into question. In an effort to minimize the total number of evaluations by maximizing the amount of learning that takes place during an evaluation, a new crossover operator for genetic programming, memetic crossover, was recently introduced. This work will attempt to analyze the genealogical impact that this operator has throughout a run in an effort to understand what is really happening behind the scenes.
منابع مشابه
Genetic and Memetic Algorithms for Sequencing a New JIT Mixed-Model Assembly Line
This paper presents a new mathematical programming model for the bi-criteria mixed-model assembly line balancing problem in a just-in-time (JIT) production system. There is a set of criteria to judge sequences of the product mix in terms of the effective utilization of the system. The primary goal of this model is to minimize the setup cost and the stoppage assembly line cost, simultaneously. B...
متن کاملMemetic Crossover for Genetic Programming: Evolution Through Imitation
For problems where the evaluation of an individual is the dominant factor in the total computation time of the evolutionary process, minimizing the number of evaluations becomes critical. This paper introduces a new crossover operator for genetic programming, memetic crossover, that reduces the number of evaluations required to find an ideal solution. Memetic crossover selects individuals and c...
متن کاملNovel Knowledge Based Tabu Crossover In Genetic Algorithms
Genetic algorithms are optimisation algorithms and mimic the natural process of evolution. Important operators used in genetic algorithms are selection, crossover and mutation. Selection operator is used to select the individuals from a population to create a mating pool which will participate in reproduction process. Crossover and mutation operators are used to introduce diversity in the popul...
متن کاملDimensionality Reduction and Improving the Performance of Automatic Modulation Classification using Genetic Programming (RESEARCH NOTE)
This paper shows how we can make advantage of using genetic programming in selection of suitable features for automatic modulation recognition. Automatic modulation recognition is one of the essential components of modern receivers. In this regard, selection of suitable features may significantly affect the performance of the process. Simulations were conducted with 5db and 10db SNRs. Test and ...
متن کاملA Novel Experimental Analysis of the Minimum Cost Flow Problem
In the GA approach the parameters that influence its performance include population size, crossover rate and mutation rate. Genetic algorithms are suitable for traversing large search spaces since they can do this relatively fast and because the mutation operator diverts the method away from local optima, which will tend to become more common as the search space increases in size. GA’s are base...
متن کامل