Work

Running time analysis of the (1+1)-EA for onemax and leadingones under bit-wise noise

Year: 2017

Type: conference-paper

Abstract: Previous running time analyses of evolutionary algorithms (EAs) in noisy environments often studied the one-bit noise model, which flips a randomly chosen bit of a solution before evaluation. In this paper, we study a natural extension of one-bit noise, the bit-wise noise model, which independently flips each bit of a solution with some probability. We analyze the running time of the (1+1)-EA solving OneMax and LeadingOnes under bit-wise noise for the first time, and derive the ranges of the noise level for polynomial and super-polynomial running time bounds. The analysis on LeadingOnes under bit-wise noise can be easily transferred to one-bit noise, and improves the previously known results.

Source: Genetic and Evolutionary Computation Conference (GECCO)

DOI: 10.1145/3071178.3071347

Authors: Chao Qian, Chao Bian, Wu Jiang, Ke Tang

Institution: University of Science and Technology of China

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities

Awards: G3637990122, G6503845381, G6741969961, G758489918