Geodesy

Evolutionary algorithm

Metaheuristic

Algorithm

Benchmark (surveying)

Linguistics

Demography

Philosophy

Multi-swarm optimization

Fitness function

Selection (genetic algorithm)

Set (abstract data type)

Particle swarm optimization

Computer science

Mathematical optimization

Swarm behaviour

Mathematics

Genetic algorithm

Binary search algorithm

Geography

Feature selection

Feature (linguistics)

Surrogate model

Population

Sociology

Artificial intelligence

Search algorithm

Programming language

Binary number

Machine learning

Arithmetic

Applied Soft Computing

The evolutionary algorithms (EAs) have been shown favorable performance for feature selection. However, a large number of evaluations are required through the EAs. Thus, they will be inappropriate to optimize feature selection when the size of data set is large. In this paper, we propose a multi-surrogate assisted binary particle swarm optimization, denoted as MS-assisted DBPSO. Two surrogate models are trained, which are utilized to approximate the fitness values of the individuals in two sub-populations, respectively. After that, a new population will be generated by the communication between the two sub-populations. Furthermore, dynamic transfer function is proposed in this paper to balance global and local search aiming to find optimal solution with limited computational resource. The experimental results on binary benchmark functions and the feature selection in the UCI data sets demonstrate that our proposed method is efficient on reducing running time and prediction error.

Multi-surrogate assisted binary particle swarm optimization algorithm and its application for feature selection