Distributed evolutionary algorithms and their models: A survey of the state-of-the-art
The increasing complexity of real-world optimization problems raises new challenges to
evolutionary computation. Responding to these challenges, distributed evolutionary …
evolutionary computation. Responding to these challenges, distributed evolutionary …
Deap: A python framework for evolutionary algorithms
FM De Rainville, FA Fortin, MA Gardner… - Proceedings of the 14th …, 2012 - dl.acm.org
DEAP (Distributed Evolutionary Algorithms in Python) is a novel volutionary computation
framework for rapid prototyping and testing of ideas. Its design departs from most other …
framework for rapid prototyping and testing of ideas. Its design departs from most other …
A distributed swarm optimizer with adaptive communication for large-scale optimization
Large-scale optimization with high dimensionality and high computational cost becomes
ubiquitous nowadays. To tackle such challenging problems efficiently, devising distributed …
ubiquitous nowadays. To tackle such challenging problems efficiently, devising distributed …
Distributed cooperative co-evolution with adaptive computing resource allocation for large scale optimization
Through introducing the divide-and-conquer strategy, cooperative co-evolution (CC) has
been successfully employed by many evolutionary algorithms (EAs) to solve large-scale …
been successfully employed by many evolutionary algorithms (EAs) to solve large-scale …
Evolutionary optimization: Pitfalls and booby traps
Evolutionary computation (EC), a collective name for a range of metaheuristic black-box
optimization algorithms, is one of the fastest-growing areas in computer science. Many …
optimization algorithms, is one of the fastest-growing areas in computer science. Many …
A federated learning-inspired evolutionary algorithm: Application to glucose prediction
In this paper, we propose an innovative Federated Learning-inspired evolutionary
framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is …
framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is …
Parallel approaches for multiobjective optimization
This chapter presents a general overview of parallel approaches for multiobjective
optimization. For this purpose, we propose a taxonomy for parallel metaheuristics and exact …
optimization. For this purpose, we propose a taxonomy for parallel metaheuristics and exact …
A parallel divide-and-conquer-based evolutionary algorithm for large-scale optimization
Large-scale optimization problems that involve thousands of decision variables have
extensively arisen from various industrial areas. As a powerful optimization tool for many …
extensively arisen from various industrial areas. As a powerful optimization tool for many …
Optimal inversion of Manning's roughness in unsteady open flow simulations using adaptive parallel Genetic algorithm
L Yao, Y Peng, X Yu, Z Zhang, S Luo - Water Resources Management, 2023 - Springer
Manning's roughness coefficient (n) is a comprehensive indicator of flow resistance, and
significantly affects the accuracy of one-dimensional (1D) unsteady flow simulations. Most …
significantly affects the accuracy of one-dimensional (1D) unsteady flow simulations. Most …