Experience-Based Evolutionary Algorithms for Expensive Optimization

by   Xunzhao Yu, et al.

Optimization algorithms are very different from human optimizers. A human being would gain more experiences through problem-solving, which helps her/him in solving a new unseen problem. Yet an optimization algorithm never gains any experiences by solving more problems. In recent years, efforts have been made towards endowing optimization algorithms with some abilities of experience learning, which is regarded as experience-based optimization. In this paper, we argue that hard optimization problems could be tackled efficiently by making better use of experiences gained in related problems. We demonstrate our ideas in the context of expensive optimization, where we aim to find a near-optimal solution to an expensive optimization problem with as few fitness evaluations as possible. To achieve this, we propose an experience-based surrogate-assisted evolutionary algorithm (SAEA) framework to enhance the optimization efficiency of expensive problems, where experiences are gained across related expensive tasks via a novel meta-learning method. These experiences serve as the task-independent parameters of a deep kernel learning surrogate, then the solutions sampled from the target task are used to adapt task-specific parameters for the surrogate. With the help of experience learning, competitive regression-based surrogates can be initialized using only 1d solutions from the target task (d is the dimension of the decision space). Our experimental results on expensive multi-objective and constrained optimization problems demonstrate that experiences gained from related tasks are beneficial for the saving of evaluation budgets on the target problem.


PAMELI: A Meta-Algorithm for Computationally Expensive Multi-Objective Optimization Problems

We present an algorithm for multi-objective optimization of computationa...

A Novel Surrogate-assisted Evolutionary Algorithm Applied to Partition-based Ensemble Learning

We propose a novel surrogate-assisted Evolutionary Algorithm for solving...

Enhancing SAEAs with Unevaluated Solutions: A Case Study of Relation Model for Expensive Optimization

Surrogate-assisted evolutionary algorithms (SAEAs) hold significant impo...

Are Evolutionary Algorithms Safe Optimizers?

We consider a type of constrained optimization problem, where the violat...

Hostile Intent Identification by Movement Pattern Analysis: Using Artificial Neural Networks

In the recent years, the problem of identifying suspicious behavior has ...

Retaining Experience and Growing Solutions

Generally, when genetic programming (GP) is used for function synthesis ...

Code Repositories


Experience-Based Surrogate-Assisted Evolutionary Algorithm Framework for Expensive Optimization Problems.

view repo

Please sign up or login with your details

Forgot password? Click here to reset