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A review of population-based metaheuristics for large-scale black-box global optimization—Part I
Scalability of optimization algorithms is a major challenge in co** with the ever-growing
size of optimization problems in a wide range of application areas from high-dimensional …
size of optimization problems in a wide range of application areas from high-dimensional …
A review of population-based metaheuristics for large-scale black-box global optimization—Part II
This article is the second part of a two-part survey series on large-scale global optimization.
The first part covered two major algorithmic approaches to large-scale optimization, namely …
The first part covered two major algorithmic approaches to large-scale optimization, namely …
Evolutionary algorithms for parameter optimization—thirty years later
Thirty years, 1993–2023, is a huge time frame in science. We address some major
developments in the field of evolutionary algorithms, with applications in parameter …
developments in the field of evolutionary algorithms, with applications in parameter …
[HTML][HTML] A model-based evolutionary algorithm for home health care scheduling
In this paper, for the first time a model-based evolutionary algorithm is presented for a real-
life Home Health Care Routing and Scheduling Problem (HHCRSP). The algorithm …
life Home Health Care Routing and Scheduling Problem (HHCRSP). The algorithm …
Optimal mixing evolutionary algorithms
A key search mechanism in Evolutionary Algorithms is the mixing or juxtaposing of partial
solutions present in the parent solutions. In this paper we look at the efficiency of mixing in …
solutions present in the parent solutions. In this paper we look at the efficiency of mixing in …
Parameter-less population pyramid
Real world applications of evolutionary techniques are often hindered by the need to
determine problem specific parameter settings. While some previous methods have reduced …
determine problem specific parameter settings. While some previous methods have reduced …
Cooperative coevolution with knowledge-based dynamic variable decomposition for bilevel multiobjective optimization
Many practical multiobjective optimization problems have a nested bilevel structure in
variables, which can be modeled as bilevel multiobjective optimization problems (BLMOPs) …
variables, which can be modeled as bilevel multiobjective optimization problems (BLMOPs) …
Improving test case generation for rest apis through hierarchical clustering
With the ever-increasing use of web APIs in modern-day applications, it is becoming more
important to test the system as a whole. In the last decade, tools and approaches have been …
important to test the system as a whole. In the last decade, tools and approaches have been …
Partition crossover for pseudo-boolean optimization
A partition crossover operator is introduced for use with NK landscapes, MAX-kSAT and for
all k-bounded pseudo-Boolean functions. By definition, these problems use a bit …
all k-bounded pseudo-Boolean functions. By definition, these problems use a bit …
A review on probabilistic graphical models in evolutionary computation
Thanks to their inherent properties, probabilistic graphical models are one of the prime
candidates for machine learning and decision making tasks especially in uncertain domains …
candidates for machine learning and decision making tasks especially in uncertain domains …