IEEE Member-only icon

Dynamic Selection of Evolutionary Algorithm Operators Based on Online Learning and Fitness Landscape Metrics

Dynamic Selection of Evolutionary Algorithm Operators Based on Online Learning and Fitness Landscape Metrics

Dynamic Selection of Evolutionary Algorithm Operators Based on Online Learning and Fitness Landscape Metrics

115 views

IEEE Computational Intelligence Society

Create Account or Sign In to post comments

#Pietro Consoli #IEEE CIS Junior Webinar Series #2015 #Evolutionary Algorithm #Operator #Dynamic Selection; MAENS algorithm #

The author presents an introduction to dynamic selection of Evolutionary Algorithm operators (crossover) and their selection based on online learning and fitness landscape metrics. The example is using the Memetic Algorithm with Extended Neighborhood Search (MAENS).

The author presents an introduction to dynamic selection of Evolutionary Algorithm operators (crossover) and their selection based on online learning and fitness landscape metrics. The example is using the Memetic Algorithm with Extended Neighborhood Search (MAENS).

May 18, 2015

Next Up

01:17:27

SSCI2022 - Plenary Talk 1 - Federated Learning, Knowledge Transfer, and Knowledge Distillation: At the Junction of Green Machine Learning and Granular Computing

01:04:09

SSCI2022 - Plenary Talk 2 - Evolutionary Machine Learning: Research, Applications and Challenges

01:05:42

SSCI2022 - Plenary Talk 3 - Composing Music by Evolutionary Computation

01:01:26

SSCI2022 - Plenary Talk 4 - Learning with DNNs: Optimizer, Architecture and Formulation

01:03:21

SSCI2022 - Plenary Talk 5 - Lifelong Learning on the Edge with Adaptive Resonance Theory and Incremental Cluster Validity Indices

01:05:55

Democratizing NLP: Considerations From Resources To Algorithms