Several different ways exist for approaching hard optimization problems. Mathematical
programming techniques, including (integer) linear programming based methods, and …

This paper provides a systematic study of the technologies and algorithms associated with
the implementation of multiobjective evolutionary algorithms (MOEAs) for the solution of the …

Introduction Memetic Algorithms (MAs) are population-based metaheuristics composed of
an evolutionary framework and a set of local search algorithms which are activated within …

This article examines the effect of different configuration issues of the Multiobjective
Evolutionary Algorithms on the efficient frontier formulation for the constrained portfolio …

Biomimicking, which has been practiced since the dawn of civilization, is evidenced by
adobe bricks dated back millennia ago. One vivid example is flight, inspired by flapping …

SCI 379 - Memetic Algorithms in Continuous Optimization Page 1 Chapter 8 Memetic
Algorithms in Continuous Optimization Carlos Cotta and Ferrante Neri 8.1 Introduction and …

Using a new exact synthesizer that automatically induces minimal universal boolean
function libraries, we introduce two indicators for comparing their expressiveness: the first …

Introduction As mentioned in previous chapters in this volume, metaheuristics (and
specifically MAs) have a part of their raison d'etre in practically solving problems whose …

Multiobjective optimization (MO) is the problem of simultaneously optimizing two or more
conflicting objectives subject to certain constraints. Many real-world problems involve …

This study presents the development of a LLM-based Q&A chatbot model for interpreting
complex civil engineering regulations in South Korea. Leveraging large language model …