In this paper, binary variants of the ant lion optimizer (ALO) are proposed and used to select
the optimal feature subset for classification purposes in wrapper-mode. ALO is one of the …

Advancements in medical technology have created numerous large datasets including
many features. Usually, all captured features are not necessary, and there are redundant …

Fitness landscapes were proposed in 1932 as an abstract notion for understanding
biological evolution and were later used to explain evolutionary algorithm behaviour. The …

This article suggests a multimodal multiobjective evolutionary algorithm with dual clustering
in decision and objective spaces. One clustering is run in decision space to gather nearby …

Since most metaheuristic algorithms for continuous search space have been developed, a
number of transfer functions have been proposed including S-shaped, V-shaped, linear, U …

Attribute selection plays a vital role in optimization and machine learning that involves huge
datasets. Classification accuracy of any learning model depends on the dimensionality of …

In this paper, a modification of the newly proposed antlion optimization (ALO) is introduced
and applied to feature selection relied on the Lèvy flights. ALO method is one of the …

In this paper, we show that when dealing with multi-objective combinatorial optimisation
problems, the search, in different executions of a multi-objective evolutionary algorithm …

Local search has gained its popularity in addressing multi-objective combinatorial
optimisation problems (MOCOPs) within the communities of evolutionary computation and …

We consider and discuss the ways in which search landscapes might contribute to the future
of explainable artificial intelligence (XAI), and vice versa. Landscapes are typically used to …