Name
Title | ||
|---|---|---|
Engineering Fitness Inheritance And Co-Operative Evolution Into State-Of-The-Art Optimizers |
Abstract | ||
|---|---|---|
The engineering of stochastic black box optimization methods, particularly evolutionary algorithms (EAs), represents the most common and successful approach to solve lareg-scale parameter optimization problems. Currently several strategies are being explored to improve performance when the number of parameters is large (e.g. 1000 parameters). Prominent among these techniques are variants of differential evolution, while an algorithm engineering strategy being explored is 'co-operative co-evolution' (CC), which involves successively optimizing subsets of the design parameters, with an organized approach occasionally reconciling these 'subspace' optimizations. Recent work has shown that combining CC with fitness inheritance (FI) - a technique heretofore rarely explored in the context of large-scale optimization - can reliably lead to better performance. However that work was done in the context of a simple underlying EA (allowing us to be more confident that the benefits were due primarily to the combination of CC and FI). Here we explore the extent to which CC and FI provides added value when engineered together in the context of more sophisticated, so-called state of the art underlying algorithms, pre-adorned with a variety of additional enhancements. To that end, in this paper we explore SaNSDE, and DECC-DML - two recent high-performance techniques in the field of large-scale optimization. We also explore two basic adaptive parameter setting strategies for the FI component. We find that engineering FI (and CC, where it otherwise wasn't) into these algorithms can provides either competitive or improved results |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SSCI.2015.238 | 2015 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI) |
Keywords | Field | DocType |
evolutionary computation,algorithm design and analysis,optimization,sociology,statistics | Probabilistic-based design optimization,Algorithm engineering,Evolutionary algorithm,Computer science,Meta-optimization,Evolutionary computation,Multi-swarm optimization,Differential evolution,Artificial intelligence,Engineering optimization,Machine learning | Conference |
Citations | PageRank | References |
2 | 0.37 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| aboubakar hameed | 1 | 2 | 0.37 |
| Anna V. Kononova | 2 | 42 | 4.38 |
| David W. Corne | 3 | 2161 | 152.00 |