[LIVRE][B] Handbook of memetic algorithms
Memetic Algorithms (MAs) are computational intelligence structures combining multiple and
various operators in order to address optimization problems. The combination and …
various operators in order to address optimization problems. The combination and …
[LIVRE][B] Evolutionary learning: Advances in theories and algorithms
Many machine learning tasks involve solving complex optimization problems, such as
working on non-differentiable, non-continuous, and non-unique objective functions; in some …
working on non-differentiable, non-continuous, and non-unique objective functions; in some …
[HTML][HTML] From black-box complexity to designing new genetic algorithms
Black-box complexity theory recently produced several surprisingly fast black-box
optimization algorithms. In this work, we exhibit one possible reason: These black-box …
optimization algorithms. In this work, we exhibit one possible reason: These black-box …
Quality-diversity algorithms can provably be helpful for optimization
Quality-Diversity (QD) algorithms are a new type of Evolutionary Algorithms (EAs), aiming to
find a set of high-performing, yet diverse solutions. They have found many successful …
find a set of high-performing, yet diverse solutions. They have found many successful …
On the effect of populations in evolutionary multi-objective optimization
O Giel, PK Lehre - Proceedings of the 8th annual conference on Genetic …, 2006 - dl.acm.org
Multi-objective evolutionary algorithms (MOEAs) have become increasingly popular as multi-
objective problem solving techniques. An important open problem is to understand the role …
objective problem solving techniques. An important open problem is to understand the role …
On the impact of the mutation-selection balance on the runtime of evolutionary algorithms
PK Lehre, X Yao - Proceedings of the tenth ACM SIGEVO workshop on …, 2009 - dl.acm.org
The interplay between the mutation operator and the selection mechanism plays a
fundamental role in the behaviour of evolutionary algorithms. However, this interplay is still …
fundamental role in the behaviour of evolutionary algorithms. However, this interplay is still …
A rough-to-fine evolutionary multiobjective optimization algorithm
This article presents a rough-to-fine evolutionary multiobjective optimization algorithm based
on the decomposition for solving problems in which the solutions are initially far from the …
on the decomposition for solving problems in which the solutions are initially far from the …
[HTML][HTML] Optimizing linear functions with the (1+ λ) evolutionary algorithm—different asymptotic runtimes for different instances
We analyze how the (1+ λ) evolutionary algorithm (EA) optimizes linear pseudo-Boolean
functions. We prove that it finds the optimum of any linear function within an expected …
functions. We prove that it finds the optimum of any linear function within an expected …
Evolutionary diversity optimization and the minimum spanning tree problem
In the area of evolutionary computation the calculation of diverse sets of high-quality
solutions to a given optimization problem has gained momentum in recent years under the …
solutions to a given optimization problem has gained momentum in recent years under the …
Global versus local search: the impact of population sizes on evolutionary algorithm performance
In the field of Evolutionary Computation, a common myth that “An Evolutionary Algorithm
(EA) will outperform a local search algorithm, given enough runtime and a large-enough …
(EA) will outperform a local search algorithm, given enough runtime and a large-enough …