Designing collective behaviors for robot swarms is a difficult endeavor due to their fully distributed, highly redundant, and ever-changing nature. To overcome the challenge, a few approaches have been proposed, which can be classified as manual, semi-automatic, or automatic design. This paper is intended to be the manifesto of the automatic off-line design for robot swarms. We define the off-line design problem and illustrate it via a possible practical realization, highlight the core research questions, raise a number of issues regarding the existing literature that is relevant to the automatic off-line design, and provide guidelines that we deem necessary for a healthy development of the domain and for ensuring its relevance to potential real-world applications.

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Industrial production scheduling problems are challenges that researchers have been trying to solve for decades. Many practical scheduling problems such as the hybrid flowshop are N P-hard. As a result, researchers resort to metaheuristics to obtain e ective and e cient solutions. The traditional design process of metaheuristics is mainly manual, often metaphor-based, biased by previous experience and prone to producing overly tailored methods that only work well on the tested problems and objectives. In this paper, we use an Automatic Algorithm Design (AAD) methodology to eliminate these limitations. AAD is capable of composing algorithms from components with minimal human intervention. We test the proposed AAD for three di erent optimization objectives in the hybrid flowshop. Comprehensive computational and statistical testing demonstrates that automatically designed algorithms outperform specifically tailored state-of-the-art methods for the tested objectives in most cases.

Grammar-based automatic algorithm design has been shown to generate stochastic local search algorithms that compete with or outperform state-of-the-art methods. In such systems, algorithms are divided in components and a grammar is used to describe how to properly combine the components to create a working algorithm. In our approach, the grammar is converted in parameters and an automatic parameter configuration tool is used to find the best configuration. This approach allows to consider and hybridize different metaheuristic templates producing combinations never tested before, but this flexibility leads to a very large configuration space to explore. Is such complexity really needed to achieve state-of-the-art performance? In this paper, we investigate this question by creating grammars that allow the hybridization of stochastic local search algorithms at most two, one or zero times. We use these grammars to generate algorithms for the three most studied objectives of the permutation flowshop problem: makespan, total completion time and total tardiness. The generated algorithms are compared using benchmark sets from the literature as well as a quantitative measure of algorithm complexity using a metric based on concept directed acyclic graphs. The experiments show that our system tends to generate hybridized algorithms only when they can provide a substantial performance improvement. On the contrary, when such algorithms do not improve performance, the system generates simpler algorithms regardless of the grammar used.