ECADA 2024 - 14th Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA)

We welcome original submissions on all aspects of Evolutionary Computation for the Automated Design of Algorithms, in particular, evolutionary computation methods and other hyper-heuristics for the automated design, generation or improvement of algorithms that can be applied to any instance of a target problem domain. Relevant methods include methods that evolve whole algorithms given some initial components as well as methods that take an existing algorithm and improve it or adapt it to a specific domain. Another important aspect in automated algorithm design is the definition of the primitives that constitute the search space of hyper-heuristics. These primitives should capture the knowledge of human experts about useful algorithmic components (such as selection, mutation and recombination operators, local searches, etc.) and, at the same time, allow the generation of new algorithm variants. Examples of the application of hyper-heuristics, including genetic programming and automatic configuration methods, to such frameworks of algorithmic components are of interest to this workshop, as well as the (possibly automatic) design of the algorithmic components themselves and the overall architecture of metaheuristics. Therefore, relevant topics include (but are not limited to):
● Applications of hyper-heuristics, including general-purpose automatic algorithm configuration methods, for the design of evolutionary algorithms, other metaheuristics and other algorithms for application domains such as optimization, data mining, machine learning, image processing, engineering, cyber security, critical infrastructure protection, and bioinformatics.
● Novel hyper-heuristics, including but not limited to genetic programming based approaches, automatic configuration methods, and online, offline and life-long hyper-heuristics, with the stated goal of designing or improving the design of algorithms.
● Empirical comparison of hyper-heuristics.
● Theoretical analyses of hyper-heuristics.
● Studies on primitives (algorithmic components) that may be used by hyper-heuristics as the search space when automatically designing algorithms.
● Automatic selection/creation of algorithm primitives as a preprocessing step for the use of hyper-heuristics.
● Analysis of the trade-off between generality and effectiveness of different hyper-heuristics or of algorithms produced by a hyper-heuristic.
● Analysis of the most effective representations for hyper-heuristics (e.g., Koza style Genetic Programming versus Cartesian Genetic Programming).
● Asynchronous parallel evolution of hyper-heuristics.
For more detailed information, see the ECADA-AT-GECCO 2024 workshop website (https://bonsai.auburn.edu/ecada/GECCO2024/).