Deep reinforcement learning (DRL) has recently been adopted in a wide range of physics
and engineering domains for its ability to solve decision-making problems that were …

This paper gives an overview of meta-heuristics methods utilized within the paradigm of
multi-objective programming. This is an area of research that has undergone substantial …

Problems with multiple objectives arise in a natural fashion in most disciplines and their
solution has been a challenge to researchers for a long time. Despite the considerable …

Before we explain our motivation for writing this book, let us place its subject in a more
general context. Shape optimization can be viewed as a part of the important branch of …

In this paper, a new real coded crossover operator, called the Laplace Crossover (LX) is
proposed. LX is used in conjunction with two well known mutation operators namely the …

In this paper, a new mutation operator called power mutation (PM) is introduced for real
coded genetic algorithms (RCGA). The performance of PM is compared with two other …

Genetic algorithms (GA) are stimulated by population genetics and evolution at the
population level where crossover and mutation comes from random variables. The problems …

We present the first closed-loop separation control experiment using a novel, model-free
strategy based on genetic programming, which we call 'machine learning control'. The goal …

A systematic approach is presented to approximate the Pareto optimal front (POF) by a
response surface approximation. The data for the POF is obtained by multi-objective …

▪ Abstract This paper is a short and nonexhaustive survey of some recent developments in
optimal shape design (OSD) for fluids. OSD is an interesting field both mathematically and …