Tutorials
The role of the tutorials is to provide a platform for a more intensive scientific exchange amongst researchers interested in a particular topic and as a meeting point for the community. Tutorials complement the depth-oriented technical sessions by providing participants with broad overviews of emerging fields. A tutorial can be scheduled for 1.5 or 3 hours.
TUTORIALS LIST
Tutorial on Structural Bias in Optimization Algorithms (IJCCI)
Instructor : Anna V. Kononova, Diederick Vermetten, Niki van Stein and Fabio Caraffini
Tutorial on
Structural Bias in Optimization Algorithms
Abstract
Abstract
Benchmarking heuristic algorithms is crucial for understanding their performance under different conditions. This tutorial focuses on behaviour benchmarking, specifically addressing the so-called Structural Bias, an inherent bias in iterative heuristic optimizers. By detecting and analyzing Structural Bias, we can enhance algorithm development and identify bias-free conditions. Within this tutorial, we will define Structural Bias, present tools for its detection, discuss computational issues with such detection and visualise examples of structurally biased algorithms. We will showcase the developed toolkits and offer initial insights into mechanisms of the formation of structural bias across various optimization heuristics.
Keywords
optimisation, structural bias, benchmarking, algorithmic behaviour, demo
Aims and Learning Objectives
- convey the importance of benchmarking heuristic algorithms to comprehend their performance across different problem scenarios;
- concentrate on behaviour benchmarking, specifically delving into the concept of Structural Bias (SB) in iterative heuristic optimisers;
- enable participants to detect, analyse, and understand the occurrence and impact of Structural Bias in heuristic optimisation algorithms;
- provide insights into how detecting SB can lead to improved algorithm development and refinement;
- showcase the functionality and usage of developed toolkits as practical tools for detecting and addressing Structural Bias;
- share findings from analysing structural bias across well-known optimisation heuristics, offering insights and patterns.
Target Audience
Researchers who develop heuristic optimisation algorithms or analyse/benchmark such algorithms
Prerequisite Knowledge of Audience
- familiarity with standard heuristic optimisation algorithms and benchmarking practices
- programming examples will be provided in Python
Detailed Outline
Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. Most benchmarks are performance-based, to test algorithm performance under a wide set of conditions. There are also resource- and behaviour-based benchmarks to test algorithms' resource consumption and behaviour. In this Tutorial, we focus on behaviour benchmarking of algorithms and more specifically we focus on Structural Bias (SB).
SB is a form of bias inherent to the iterative heuristic optimiser in the search space that also affects the performance of the optimisation algorithm. Detecting whether, when and what type of SB occurs in a heuristic optimisation algorithm can provide guidance on what needs to be improved in these algorithms, besides helping to identify conditions under which such bias would not occur.
In the tutorial, we first give the problem definition of detecting and identifying different types of structural bias, including many visual examples. We then introduce different methods for bias detection and demo the BIAS toolkit.
For many well-known and popular optimization heuristics, we have analysed structural bias for different hyper-parameters, we give insights into these results and show best practices to avoid SB in algorithm development.