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A Theoretical Assessment of Solution Quality in Evolutionary Algorithms for the Knapsack Problem
Nonetheless, few rigorous investigations address the quality of solutions that evolutionary algorithms may produce for the knapsack problem.
Combining Drift Analysis and Generalized Schema Theory to Design Efficient Hybrid and/or Mixed Strategy EAs
Hybrid and mixed strategy EAs have become rather popular for tackling various complex and NP-hard optimization problems.
A Further Generalization of the Finite-Population Geiringer-like Theorem for POMDPs to Allow Recombination Over Arbitrary Set Covers
Recently a finite population version of Geiringer theorem with nonhomologous recombination has been adopted to the setting of Monte-Carlo tree search to cope with randomness and incomplete information by exploiting the entrinsic similarities within the state space of the problem.
Geiringer Theorems: From Population Genetics to Computational Intelligence, Memory Evolutive Systems and Hebbian Learning
The classical Geiringer theorem addresses the limiting frequency of occurrence of various alleles after repeated application of crossover.
A Polynomial Time Approximation Scheme for a Single Machine Scheduling Problem Using a Hybrid Evolutionary Algorithm
Nowadays hybrid evolutionary algorithms, i. e, heuristic search algorithms combining several mutation operators some of which are meant to implement stochastically a well known technique designed for the specific problem in question while some others playing the role of random search, have become rather popular for tackling various NP-hard optimization problems.
Novel Analysis of Population Scalability in Evolutionary Algorithms
(1) We demonstrate rigorously that for elitist EAs with identical global mutation, using a lager population size always increases the average rate of convergence to the optimal set; and yet, sometimes, the expected number of generations needed to find an optimal solution (measured by either the maximal value or the average value) may increase, rather than decrease.
