Computing and Electronic Engineering
Ahmed Kattan portrait image


Surrogate Optimisation 

Project description

When the optimisation problem gives rise to objective functions which are prohibitively expensive to evaluate (like in aerodynamic simulation), a single optimisation case can take many minutes, hours, or even days to complete and often the whole optimisation process become infeasible. Optimisation methods based on surrogate models, have been successfully employed to tackle expensive objective functions. An attractive way to reduce the search time of EA’s when dealing with expensive objective function is to use a cheap approximation model, that can rank the population similarly as the original expensive evaluation function.


Here you can download Surrogate program (searches the NK-landscape) and compare it with other methods. 

· RBFN Surrogate Executable file. (Require Windows OS and latest .Net framework)


· Generalisation of Surrogate Model-Based Optimisation to Combinatorial and Program Spaces,

· Geometric Generalisation of Surrogate Model Based Optimisation to Combinatorial Spaces

· Evolving Optimal Agendas for Package Deal Negotiation

· Evolving Radial Basis Function Networks via GP for Estimating Fitness Values using Surrogate Models

· PSO Based on Surrogate Modeling as Meta-Search to Optimise Evolutionary Algorithms Parameters

· Agendas and Strategies for Negotiation in Dynamic Environments: A Surrogate Based Approach

· Multi-Agent Multi-Issue Negotiations with Incomplete Information: A Genetic Algorithm Based on Discrete Surrogate Approach