Convergence Properties of (μ + λ) Evolutionary Algorithms
نویسندگان
چکیده
Introduction Evolutionary Algorithms (EA) are a branch of heuristic population-based optimization tools that is growing in popularity (especially for combinatorial and other problems with poorly understood landscapes). Despite their many uses, there are no proofs that an EA will always converge to the global optimum for any general problem. Indeed, only for a set of trivial functions there are any proofs at all.
منابع مشابه
Convergence Properties of Two (μ+λ) Evolutionary Algorithms on OneMax and Royal Roads Test Functions
We present a number of bounds on convergence time for two elitist population-based Evolutionary Algorithms using a recombination operator k-Bit-Swap and a mainstream Randomized Local Search algorithm. We study the effect of distribution of elite species and population size.
متن کاملParameterized Runtime Analyses of Evolutionary Algorithms for the Euclidean Traveling Salesperson Problem
Parameterized runtime analysis seeks to understand the influence of problem structure on algorithmic runtime. In this paper, we contribute to the theoretical understanding of evolutionary algorithms and carry out a parameterized analysis of evolutionary algorithms for the Euclidean traveling salesperson problem (Euclidean TSP). We investigate the structural properties in TSP instances that infl...
متن کاملConvergence of Hypervolume-Based Archiving Algorithms II: Competetiveness
We study the convergence behavior of (μ + λ)-archiving algorithms. A (μ + λ)-archiving algorithm defines how to choose in each generation μ children from μ parents and λ offspring together. Archiving algorithms have to choose individuals online without knowing future offspring. Previous studies assumed the offspring generation to be best-case. We assume the initial population and the offspring ...
متن کاملConvergence Analysis of Evolution Strategies with Random Numbers of Offspring
Abstract. Hitting times of the global optimum for evolutionary algorithms are usually available for simple unimodal problems or for simplied algorithms. In discrete problems, the number of results that relate the convergence rate of evolution strategies to the geometry of the optimisation landscape is restricted to a few theoretical studies. This article introduces a variant of the canonical (μ...
متن کاملConvergence analysis of a self-adaptive multi-objective evolutionary algorithm based on grids
Evolutionary algorithms have been successfully applied to various multi-objective optimization problems. However, theoretical studies on multi-objective evolutionary algorithms, especially with self-adaption, are relatively scarce. This paper analyzes the convergence properties of a self-adaptive (μ+1)-algorithm. The convergence of the algorithm is defined, and the general convergence condition...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2011