Tracks

ACO-SI - Ant Colony Optimization and Swarm Intelligence

Description

Swarm Intelligence (SI) is the collective problem-solving behavior of groups of animals or artificial agents that results from the local interactions of the individuals with each other and with their environment. SI systems rely on certain key principles such as decentralization, stigmergy, self-organization, local interaction, and emergent behaviors. Since these principles are observed in the organization of social insect colonies and other animal aggregates, such as bird flocks or fish schools, SI systems are typically inspired by these natural systems.
The two main application areas of SI have been optimization and robotics. In the first category, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) constitute two of the most popular SI optimization techniques with numerous applications in science and engineering, but other SI-based optimization algorithms are possible. Papers that study and compare SI mechanisms that underly these different SI approaches, both theoretically and experimentally, are welcome. In the second category, SI has been successfully used to control large numbers of robots in a decentralized way, which increases the flexibility, robustness, and fault-tolerance of the resulting systems.

Scope

The ACO-SI Track welcomes submissions of original and unpublished work in all experimental and theoretical aspects of SI, including (but not limited to) the following areas:

• Biological foundations
• Modeling and analysis of new approaches
• Hybrid schemes with other algorithms
• Multi-swarm and self-adaptive approaches
• Constraint-handling and penalty function approaches
• Combinations with local search techniques
• Approaches to solve multi- and many-objective optimization problems
• Approaches to solve dynamic and noisy optimization problems
• Approaches to multi-modal optimization, i.e., to find multiple solutions (niching)
• Benchmarking and new empirical results
• Parallel/distributed implementations and applications
• Large-scale applications
• Software and high-performance implementations
• Theoretical and experimental research in swarm robotics
• Theoretical and empirical analysis of SI approaches to gain a better understanding of SI algorithms and to inform on the development of new, more efficient approaches
• Position papers on future directions in SI research
• Applications to machine learning and data analytics

CS - Complex Systems

Description

This track invites all papers addressing the challenges of scaling evolution up to real-life complexity. This includes both the real-life complexity of biological systems, such as artificial life, artificial immune systems, and generative and developmental systems (GDS); and the real-world complexity of physical systems, such as evolutionary robotics and evolvable hardware.

Artificial life, Artificial Immune Systems, and Generative and Developmental Systems all take inspiration from studying living systems. In each field, there are generally two main complementary goals: to better understand living systems and to use this understanding to build artificial systems with properties similar to those of living systems, such as behavior, adaptability, learning, developmental or generative processes, evolvability, active perception, communication, self-organization and cognition. The track welcomes both theoretical and application-oriented studies in the above fields. The track also welcomes models of problem-solving through (social) agent interaction, emergence of collective phenomena and models of the dynamics of ecological interactions in an evolutionary context.

Evolutionary Robotics and Evolvable Hardware study the evolution of controllers, morphologies, sensors, and communication protocols that can be used to build systems that provide robust, adaptive and scalable solutions to the complexities introduced by working in real-world, physical environments. The track welcomes contributions addressing problems from control to morphology, from single robot to collective adaptive systems. Approaches to incorporating human users into the evolutionary search process are also welcome. Contributions are expected to deal explicitly with Evolutionary Computation, with experiments either in simulation or with real robots.

ECOM - Evolutionary Combinatorial Optimization and Metaheuristics

Description

The ECOM track aims to provide a forum for the presentation and discussion of high-quality research on metaheuristics for combinatorial optimization problems. Challenging problems from a broad range of applications, including logistics, network design, bioinformatics, engineering and business have been tackled successfully with metaheuristic approaches. In many cases, the resulting algorithms represent the state-of-the-art for solving these problems. In addition to evolutionary algorithms, the class of metaheuristics includes prominent generic problem solving methods, such as tabu search, iterated local search, variable neighborhood search, memetic algorithms, simulated annealing, GRASP and ant colony optimization.

Scope

The ECOM track encourages original submissions on the application of evolutionary algorithms and metaheuristics to combinational optimization problems. The topics for ECOM include, but are not limited to:

• Representation techniques
• Neighborhoods and efficient algorithms for searching them
• Variation operators for stochastic search methods
• Search space and landscape analysis
• Comparisons between different techniques (including exact methods)
• Constraint-handling techniques
• Hybrid methods, adaptive hybridization techniques and memetic computing
• Hyper-heuristics specific to combinatorial optimization problems
• Characteristics of problems and problem instances

Notice that the submission of very narrowed case studies of real-life problems as well as highly specific theoretical results on the performance of evolutionary algorithms may be better suited to other tracks at GECCO.

EML - Evolutionary Machine Learning

Description

The Evolutionary Machine Learning (EML) track at GECCO covers advances in the theory and application of evolutionary computation methods to Machine Learning (ML) problems. Evolutionary methods can tackle many different tasks within the ML context, including problems related to supervised, unsupervised, semi-supervised, and reinforcement learning, as well as more recent topics such as transfer learning and domain adaptation, deep learning, interpretability of machine learning models, and learning with unbalanced data and missing data.

The global search performed by evolutionary methods frequently provides a valuable complement to the local search of non-evolutionary machine learning methods and combinations of the two often show particular promise in practice.

Authors are strongly encouraged to compare their approach to the state-of-the-art in non-evolutionary machine learning, where appropriate.

We encourage submissions related to theoretical advances, the development of new (or modification of existing) algorithms, as well
as application-focused papers.

Scope

More concretely, topics of interest include but are not limited to:

• Theoretical and methodological advances on EML
• Evolutionary ensemble learning
• Evolutionary representation learning, transfer learning and domain adaptation
• Learning Classifier Systems (LCS) and evolutionary rule-based systems
• Genetic Programming (GP) when applied to machine learning tasks (as opposed to function optimisation)
• Hyper-parameter tuning of machine learning (i.e. AutoML approaches) using evolutionary methods
• Evolutionary learning with a small number of examples, unbalanced data or missing data values
• Other EC (e.g. particle swarm optimisation, differential evolution) for machine learning tasks
• Evolutionary computation techniques for feature extraction, feature selection, and feature construction
• Visualising and improving the interpretability of machine learning models
• Generalisation and overfitting
• Policy search and reinforcement learning
• Analysis and robustness in stochastic, noisy, or non-stationary environments
• Scalable, parallel and distributed EML, including approaches such as high performance computing, federated learning, edge computing or GPUs/TPUs
• Non tabular data modalities (e.g. image, sound, accelerometers) and their integration
• Applications of EML (non-exhaustive list):
  • Computer vision, image processing and pattern recognition
  • Data mining
  • Bioinformatics and life sciences
  • Computer vision, image processing and pattern recognition
  • Dynamic environments, time series and sequence learning
  • Cognitive systems and cognitive modelling
  • Economic modelling
  • Cyber security

EMO - Evolutionary Multiobjective Optimization

Description

In many real-world applications, several objective functions have to be optimized simultaneously, leading to a multiobjective optimization problem (MOP) for which an single ideal solution seldomly exists. Rather, MOPs typically admit multiple compromise solutions representing different trade-offs among the objectives. Due to their applicability to a wide range of MOPs, including black-box problems, evolutionary algorithms for multiobjective optimization have given rise to an important and very active research area, known as Evolutionary Multiobjective Optimization (EMO). No continuity or differentiability assumptions are required by EMO algorithms, and problem characteristics such as nonlinearity, multimodality and stochasticity can be handled as well. Furthermore, preference information provided by a decision maker can be used to deliver a finite-size approximation to the optimal solution set (the so-called Pareto-optimal set) in a single optimization run.

Scope

The Evolutionary Multiobjective Optimization (EMO) Track is intended to bring together researchers working in this and related areas to discuss all aspects of EMO development and deployment, including (but not limited to):

• Handling of continuous, combinatorial or mixed-integer problems
• Test problems and performance assessment
• Benchmarking studies, especially in comparison to non-EMO methods
• Selection mechanisms
• Variation mechanisms
• Hybridization
• Parallel and distributed models
• Stopping criteria
• Theoretical foundations and search space analysis that bring new insights to EMO
• Implementation aspects
• Algorithm selection and configuration
• Visualization
• Preference articulation
• Interactive optimization
• Many-objective optimization
• Large-scale optimization
• Expensive function evaluations
• Constraint handling
• Uncertainty handling
• Real-world applications, where the results presented extend beyond the solving of the applied problem, bringing new and broader EMO insights

ENUM - Evolutionary Numerical Optimization

Description

The ENUM track (Evolutionary NUMerical optimization) is concerned with randomized search algorithms and continuous search spaces. The scope of the ENUM track includes, but is not limited to, stochastic methods such as differential evolution (DE), evolution strategies (ES), estimation-of-distribution algorithms (EDAs) and particle swarm optimization (PSO). The track is also concerned with the analyses of continuous search spaces to better understand the complexity of optimization problems and benchmarking of continuous optimization.

Scope

The ENUM track invites submissions that present original work regarding theoretical analysis, algorithmic design, and experimental validation of algorithms for optimization in continuous domains, including work on large-scale and budgeted optimization, handling of constraints, multi-modality, noise, uncertain and/or changing environments, and mixed-integer problems. Work that advances experimental methodology and benchmarking, problem and search space analysis is also encouraged.

Application papers reporting on solving a particular real-world optimization problem with continuous search space, with a relevant methodology, should be sent primarily to the Real-World Applications (RWA) track, with ENUM being a possible secondary track. On the other hand, if one or more "real-world-like" problems are used as a testbed for a comparison of several relevant methods, ENUM is the right primary track.

Papers dealing with theoretical analyses of evolutionary algorithms in continuous search spaces should be primarily sent to the Theory Track, possibly with ENUM as a secondary track.

GA - Genetic Algorithms

Description

The Genetic Algorithm (GA) track has always been a large and important track at GECCO. We invite submissions to the GA track that present original work on all aspects of genetic algorithms, including, but not limited to:

• Practical, methodological and foundational aspects of GAs
• Design of new GA operators including representations, fitness functions, initialization, termination, parent selection, replacement strategies, recombination, and mutation
• Design of new and improved GAs
• Fitness landscape analysis
• Comparisons with other methods (e.g., empirical performance analysis)
• Design of hybrid approaches (e.g., memetic algorithms)
• Design of tailored GAs for new application areas
• Handling uncertainty (e.g., dynamic and stochastic problems, robustness)
• Metamodeling and surrogate assisted evolution
• Interactive GAs
• Co-evolutionary algorithms
• Parameter tuning and control (including adaptation and meta-GAs)
• Constraint Handling
• Diversity management (e.g., fitness sharing and crowding, automatic speciation, spatial models such as island/diffusion)
• Bilevel and multi-level optimization
• Ensemble based genetic algorithms
• Model-Based Genetic Algorithms

As a large and diverse track, the GA track will be an excellent opportunity to present and discuss your research/application with a wide variety of experts and participants of GECCO.

GECH - General Evolutionary Computation and Hybrids

Description

General Evolutionary Computation and Hybrids is a track recognizing that EC methods are often used as part of larger complex systems or in synergy with other algorithms. We welcome high-quality contributions on a wide range of topics that do not fit exclusively into one of the other tracks.

Scope

Areas of interest include the following - but the limit should set by your creativity not ours:

• Combining EAs with mechanisms that attempt to learn how to control or to coordinate a set of algorithms, such as parameter tuning, parameter control, and hyper-heuristics,
• Combining EAs with machine learning algorithms (including reinforcement learning and deep learning), e.g., for automated algorithm selection and configuration,
• Surrogate-based or surrogate-assisted optimization of expensive fitness functions, including multi-fidelity approaches,
• Combining different approaches for creating or improving solutions such as co-evolution, neuro-evolution, memetic algorithms, and other hybrids,
• Algorithms that can handle dynamic and stochastic environments, or for settings that enable parallel evaluations,
• Statistical analysis and visualization techniques aimed at understanding problem spaces or the performance and behavior of optimization techniques, including instance space analysis and landscape analysis, as well as
• Toolboxes for better benchmarking of evolutionary algorithms.

GP - Genetic Programming

Description

Genetic Programming is an evolutionary computation technique that automatically generates solutions/programs to solve a given problem. In GP, various representations have been used, such as tree structures, linear sequences of code, graphs and grammars. Provided that a suitable fitness function is devised, computer programs solving the given problem emerge, without the need for humans to explicitly program the computer. The GP track invites original contributions on all aspects of evolutionary generation of computer programs or other executable structures for specific tasks.

Scope

Advances in genetic programming include but are not limited to:

• Analysis: Information Theory, Complexity, Run-time, Visualization, Fitness Landscape, Generalisation, Domain adaptation
• Synthesis: Programs, Algorithms, Circuits, Systems
• Applications: Classification, Clustering, Control, Data mining, Big-Data analytics, Regression, Semi-supervised Learning, Policy search, Prediction, Continuous and Combinatorial Optimisation, Streaming Data, Design, Inductive Programming, Computer Vision, Feature Engineering and Feature Selection, Natural Language Processing
• Environments: Static, Dynamic, Interactive, Uncertain
• Operators: Replacement, Selection, Crossover, Mutation, Variation
• Performance: Surrogate functions, Multi-Objective, Coevolutionary, Human Competitive, Parameter Tuning
• Populations: Demes, Diversity, Niches
• Programs: Decomposition, Modularity, Semantics, Simplification, Software Improvement, Bug Repair, Software/Program Testing
• Programming Languages: Imperative, Declarative, Object-oriented, Functional
• Representations: Cartesian, Grammatical, Graphs, Linear, Rules, Trees, Geometric and Semantic
• Systems: Autonomous, Complex, Developmental, Gene Regulation, Parallel, Self-Organizing, Software

NE - Neuroevolution

Description

Neuroevolution is a machine learning approach that applies evolutionary computation (EC) to constructing artificial neural networks (NNs). Compared with other neural network training methods, Neuroevolution is highly general and allows learning without explicit targets, with arbitrary neural models and network structures. Neuroevolution has been successfully used to address challenging tasks in a wide range of areas, such as reinforcement learning, supervised learning, unsupervised learning, image analysis, computer vision, and natural language processing.

The Neuroevolution track at GECCO aims to encourage knowledge exchange between interested researchers in this area. It covers advances in the theory and applications of Neuroevolution, including all different EC methods for evolving all types of neural networks, alone and in combination with other neural learning algorithms. Authors are invited to submit their original and unpublished work to this track.

Scope

More concretely, topics of interest include but are not limited to:

• Neuroevolution algorithms involving:
  • Any EC method, e.g. genetic algorithms, evolutionary strategy, and genetic programming, particle swarm optimisation, differential evolution, meta-heuristics, Quality-Diversity, and hybrid methods.
  • Any type of neural networks, e.g. Convolutional neural network (CNN), Recurrent neural network (RNN), Long short-term memory (LSTM), Deep Belief Network (DBN), transformers, and autoencoders.
  • Evolutionary neural architecture search
  • Optimisation of network hyperparameters, activation and loss functions, learning dynamics, data augmentation, and initialisation
  • Novel candidate representations
  • Novel search mechanisms
  • Novel fitness functions
  • Surrogate assisted Neuroevolution
  • Methods for improving efficiency
  • Methods for improving regularisation
  • Multi-objective Neuroevolution
  • Neuroevolution for reinforcement learning, supervised learning, unsupervised learning
  • Neuroevolution for transfer learning, one-short learning, few-short learning, multitask learning
  • Parallelised and distributed realisations of Neuroevolution
  • Combinations of Neuroevolution and other neural learning algorithms
  • Interpretable/explainable model learning
• Applications of Neuroevolution:
  • Computer vision, image processing and pattern recognition
  • Text mining, natural language processing
  • Speech recognition
  • Neural Architecture Search
  • Machine translation
  • Medical and biological problems
  • Evolutionary robotics
  • Artificial life
  • Time series analysis
  • Cyber security
  • Scheduling and combinatorial optimization
  • Healthcare
  • Finance, fraud detection and business
  • Social media data analysis
  • Game playing
  • Visualisation

RWA - Real World Applications

Description

The Real-World Applications (RWA) track welcomes rigorous experimental, computational and/or applied advances in evolutionary computation (EC) in any discipline devoted to the study of real-world problems. The RWA track covers also real-world problems arising in creative arts, including design, games, and music (having been merged with the former track DETA - Digital Entertainment Technologies and Arts). The aim is to bring together contributions from the diverse application domains into a single event. The focus is on applications including but not limited to:

• Papers that present novel developments of EC, grounded in real-world problems.
• Papers that present new applications of EC to real-world problems.
• Papers that analyse the features of real-world problems, as a basis for designing EC solutions.
• Papers that would fall into the DETA domain, such as ones focussing on aesthetic measurement and control, biologically-inspired creativity, interactive environments and games, composition, synthesis and generative arts.

All contributions should be original research papers demonstrating the relevance and applicability of EC within a real-world problem. Papers covering multiple disciplines are welcome; we encourage the authors of such papers to write and present them in a way that allows researchers from other fields to grasp the main results, techniques, and their potential applications. Papers on novel EC research problems and novel application domains of the arts, music, and games are especially encouraged.

Scope

The real-world applications track is open to all domains and all industries.

SBSE - Search-Based Software Engineering

Description

Search-Based Software Engineering (SBSE) is the application of search algorithms and strategies to the solution of software engineering problems. Evolutionary computation is a foundation of SBSE, and since 2002 the SBSE track at GECCO has provided the unique opportunity to present SBSE research in the widest context of the evolutionary computation community. Last but not least, participating to the SBSE track and, more generally, to GECCO allow to be informed by advances in evolutionary computation, new cutting edge metaheuristic ideas, novel search strategies, approaches and findings.

We invite papers that address problems in the software engineering domain through the use of heuristic search. We particularly encourage papers demonstrating novel applications and adaptations of existing or new search strategies to software engineering problems framed as optimization tasks. While empirical results are important, papers that do not contain strong empirical results - but instead present new sound approaches, concepts, or theory in the search-based software engineering area - are also very welcome.

Moreover, we also encourage the submission of both full papers and poster-only papers describing negative results as well as industrial reports on the practical use of search-based solution approaches. Moreover poster-only papers presenting frameworks or tools for search-based software engineering are also welcome.

Scope

As an indication of the wide scope of the field, search techniques include, but are not limited to:

• Ant Colony Optimisation
• Automatically configured and Tuned Algorithms
• Estimation of Distribution Algorithms
• Evolutionary Computation
• Genetic Programming
• Hybrid Algorithms and Neuroevolution
• Hyper-heuristics
• Iterated Local Search
• Particle Swarm Optimisation
• Simulated Annealing
• Tabu Search
• Variable Neighbourhood Search

The software engineering tasks to which they are applied are drawn from throughout the engineering lifecycle and include, but are not limited to:

• Bug fixing
• Creating Recommendation Systems to Support Life Cycle (Software Requirement, Design, Development, Evolution and Maintenance, etc.)
• Developing Dynamic Service-Oriented and Mobile Systems
• Enabling Self-Configuring/Self-Healing/Self-Optimising Software Systems
• Network Design and Monitoring
• Predictive Modelling and Analytics for Software Engineering Tasks
• Project Management and Planning
• Testing including test data generation, regression test optimisation, test suite evolution
• Requirements Engineering
• Software Evolution and Maintenance
• Program Repair
• Software Security
• Software Transplantation
• System and Software Integration and Verification
• Uncertainty Processing in Software Life Cycle
• Software Architecture

THEORY - Theory

Description

The theory track welcomes all papers performing theoretical analyses or concerning theoretical aspects in evolutionary computation and related areas. Results can be proven with mathematical rigor or obtained via a thorough experimental investigation.

In addition to traditional areas in evolutionary computation like Genetic and Evolutionary Algorithms, Evolutionary Strategies, and Genetic Programming we also highly welcome theoretical papers in Artificial Life, Ant Colony Optimization, Swarm Intelligence, Estimation of Distribution Algorithms, Generative and Developmental Systems, Evolutionary Machine Learning, Search Based Software Engineering, Population Genetics, and more.

Scope

Topics include (but are not limited to):

• analytical methods like drift analysis, fitness levels, Markov chains, large deviation bounds,
• dynamic and static parameter choices,
• fitness landscapes and problem difficulty,
• population dynamics,
• problem representation,
• runtime analysis, black-box complexity, and alternative performance measures,
• single- and multi-objective problems,
• statistical approaches,
• stochastic and dynamic environments,
• variation and selection operators.

Papers submitted to the theory track may contain an appendix to give additional information. The appendix will not be part of the proceedings, and is consulted only at the discretion of the program committee. All technical details necessary for a proper evaluation must be contained in the 8-page submission or in the appendix, including full proofs and/or complete descriptions of experiments.