Many machine learning tasks involve solving complex optimization problems, such as
working on non-differentiable, non-continuous, and non-unique objective functions; in some …

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This paper presents a real-coded memetic algorithm that applies a crossover hill-climbing to
solutions produced by the genetic operators. On the one hand, the memetic algorithm …

Recombination (or called crossover) operators are a kind of characterizing feature of
evolutionary algorithms (EAs). The usefulness of recombination operators has been verified …

This paper identifies five distinct mechanisms by which a population-based algorithm might
have an advantage over a solo-search algorithm in classical optimization. These …

Understanding the impact of crossover on performance is a major problem in the theory of
genetic algorithms (GAs). We present new insight on working principles of crossover by …

We reinvestigate a fundamental question: How effective is crossover in genetic algorithms in
combining building blocks of good solutions? Although this has been discussed …

This paper presents a high-quality very large scale integration (VLSI) global router in X-
architecture, called XGRouter, that heavily relies on integer linear programming (ILP) …

One of the most controversial yet enduring hypotheses about what genetic algorithms (GAs)
are good for concerns the idea that GAs process building-blocks. More specifically, it has …

Unique input–output (UIO) sequences have important applications in conformance testing of
finite state machines (FSMs). Previous experimental and theoretical research has shown …