Evolutionary computation for expensive optimization: A survey
Expensive optimization problem (EOP) widely exists in various significant real-world
applications. However, EOP requires expensive or even unaffordable costs for evaluating …
applications. However, EOP requires expensive or even unaffordable costs for evaluating …
A survey on evolutionary computation for complex continuous optimization
Complex continuous optimization problems widely exist nowadays due to the fast
development of the economy and society. Moreover, the technologies like Internet of things …
development of the economy and society. Moreover, the technologies like Internet of things …
Evolutionary deep learning: A survey
As an advanced artificial intelligence technique for solving learning problems, deep learning
(DL) has achieved great success in many real-world applications and attracted increasing …
(DL) has achieved great success in many real-world applications and attracted increasing …
A meta-knowledge transfer-based differential evolution for multitask optimization
Knowledge transfer plays a vastly important role in solving multitask optimization problems
(MTOPs). Many existing methods transfer task-specific knowledge, such as the high-quality …
(MTOPs). Many existing methods transfer task-specific knowledge, such as the high-quality …
Learning-aided evolution for optimization
Learning and optimization are the two essential abilities of human beings for problem
solving. Similarly, computer scientists have made great efforts to design artificial neural …
solving. Similarly, computer scientists have made great efforts to design artificial neural …
Distributed differential evolution with adaptive resource allocation
Distributed differential evolution (DDE) is an efficient paradigm that adopts multiple
populations for cooperatively solving complex optimization problems. However, how to …
populations for cooperatively solving complex optimization problems. However, how to …
Evolutionary computation for intelligent transportation in smart cities: A survey
As the population in cities continues to increase, large-city problems, including traffic
congestion and environmental pollution, have become increasingly serious. The …
congestion and environmental pollution, have become increasingly serious. The …
A bi-objective knowledge transfer framework for evolutionary many-task optimization
Many-task problem (MaTOP) is a kind of challenging multitask optimization problem with
more than three tasks. Two significant issues in solving MaTOPs are measuring intertask …
more than three tasks. Two significant issues in solving MaTOPs are measuring intertask …
A multipopulation multiobjective ant colony system considering travel and prevention costs for vehicle routing in COVID-19-like epidemics
As transportation system plays a vastly important role in combatting newly-emerging and
severe epidemics like the coronavirus disease 2019 (COVID-19), the vehicle routing …
severe epidemics like the coronavirus disease 2019 (COVID-19), the vehicle routing …
Region encoding helps evolutionary computation evolve faster: A new solution encoding scheme in particle swarm for large-scale optimization
In the last decade, many evolutionary computation (EC) algorithms with diversity
enhancement have been proposed to solve large-scale optimization problems in big data …
enhancement have been proposed to solve large-scale optimization problems in big data …