- Written by Thomas Weise

Special Issue on Benchmarking of Computational Intelligence Algorithms

Applied Soft Computing by Elsevier B.V.

http://iao.hfuu.edu.cn/bocia-asoc-si

Computational Intelligence (CI) is a huge and expanding field which is rapidly gaining importance, attracting more and more interests from both academia and industry. It includes a wide and ever-growing variety of optimization and machine learning algorithms, which, in turn, are applied to an even wider and faster growing range of different problem domains. For all of these domains and application scenarios, we want to pick the best algorithms. Actually, we want to do more, we want to improve upon the best algorithm. This requires a deep understanding of the problem at hand, the performance of the algorithms we have for that problem, the features that make instances of the problem hard for these algorithms, and the parameter settings for which the algorithms perform the best. Such knowledge can only be obtained empirically, by collecting data from experiments, by analyzing this data statistically, and by mining new information from it. Benchmarking is the engine driving research in the fields of optimization and machine learning for decades, while its potential has not been fully explored. Benchmarking the algorithms of Computational Intelligence is an application of Computational Intelligence itself! This special issue of the EI/SCIE-indexed Applied Soft Computing journal published by Elsevier B.V. solicits novel contributions from this domain according to the topics of interest listed below.

Here you can download the Call for Papers (CfP) in PDF format and here as plain text file.

- Written by Thomas Weise

One of my fundamental research interests is how we can determine which optimization algorithm is good for which problem.

Unfortunately, answering this question is quite complicated. For most practically relevant problems, we need to find a trade-off between the (run)time we can invest in getting a good solution against the quality of said solution. Furthermore, the performance of almost all algorithms cannot just be a described by single pair of "solution quality" and "time needed to get a solution of that quality". Instead, these (anytime) algorithms start with an initial (often not-so-good) guess about the solution and then improve it step-by-step. In other words, their runtime behavior can be described as something like a function relating solution quality to runtime. But not a real function, since a) many algorithms are randomized, meaning that they behave differently every time you use them, even with the same input data, and b) an algorithm will usually behave different on different instances of an optimization problem type.

This means that we need to do a lot of experiments: We need to apply an optimization algorithm multiple times to a given optimization problem instance in order to "average out" the randomness. Each time, we need to collect data about the whole runtime behavior, not just the final results. Then we need to do this for multiple instances with different features in order to learn about how, e.g., the scale of a problem influences the algorithm behavior. This means that we will quite obtain a lot of data from many algorithm setups on many problem instances. The question that researchers face is thus "How can we extract useful information from that data?" How can we obtain information which helps us to improve our algorithms? How can we get data from which we can learn about the weaknesses of our methods so that we can improve them?

In this research presentation, I discuss my take on this subject. I introduce a process for automatically discovering the reasons why a certain algorithm behaves as it does and why a problem instance is harder for a set of algorithms than another. This process has already been implemented in our open source optimizationBenchmarking.org framework.

Read more: Research Talk: Automating Scientific Research in Optimization

- Written by Thomas Weise

Some time ago, I discussed why global optimization with an Evolutionary Algorithm (EA) is not necessarily better than local search. Actually, I get asked the question *"Why should I use an EA?"* quite a few times. Thus, today, it is time to write down a few ideas about why and why not you may benefit from using an EA. I tried to be objective, which is not entirely easy since I work in that domain.

- Written by Thomas Weise

Currently, two of the leading industry nations, Germany and China, are pushing their industry to increase a higher degree of automation. Automation is among the key technologies of concepts such as *Industry 4.0* and *Made in China 2025* [中国制造2025]. The goal is not automation in the traditional sense, i.e., the fixed and rigid implementation of static processes which are to be repeated millions of times in exactly the same way. Instead, *decisions* should be automated, i.e., the machinery carrying out processes in production and logistics should dynamically decide what to do based on its environment and its current situation. In other words, these machines should become intelligent.

As a researcher in optimization and operations research, this idea is not new to me. Actually, this is exactly the goal of work and it has been the goal for the past seven decades – with one major difference: the *level* at which the automated, intelligent decision process takes place. In this article I want to shortly discuss my point of view on this matter.

Read more: Intelligent Production and Logistics: The Viewpoint of Optimization

- Written by Thomas Weise

In an Inductive Program Synthesis (IPS) problem, a set of input/output data examples are given and the task is to find a program which can produce the desired outputs for the given inputs. Recently, researchers from the University of Cambridge and Microsoft Research have submitted a paper to the 5th International Conference on Learning Representations (ICLR'17) on *DeepCoder*, a new approach to IPS, i.e., to the automatic synthesis of programs. This new technology has goals similar to our work on program synthesis, but achieves them with entirely different means.

Read more: Algorithm Synthesis: Deep Learning and Genetic Programming

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