Despite significant advantages in theory of evolutionary computation, many papers related to evolutionary algorithms still lack proper analysis and limit themselves by rather vague reflections on why making a certain design choice improves the ...

Several important optimization problems in the area of vehicle routing can be seen as variants of the classical Traveling Salesperson Problem (TSP). In the area of evolutionary computation, the Traveling Thief Problem (TTP) has gained increasing ...

The Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) has shown high-performance levels when solving complicated multi-objective optimization problems. However, its adaptation for dealing with constrained multi-objective ...

Node-Depth Based Encoding is a representation for Evolutionary Algorithms applied to problems modelled by trees, storing nodes and their respective depths in an appropriately ordered list. This representation was highlighted by the results obtained, ...

Graph representations promise several desirable properties for Genetic Programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift. Each graph GP technique provides a ...

The weight maximization problem (WMP) is the problem of finding the word of highest weight on a weighted finite state automaton (WFA). It is an essential question that emerges in many optimization problems in automata theory. Unfortunately, the general ...

In the contemporary big data realm, Deep Neural Networks (DNNs) are evolving towards more complex architectures to achieve higher inference accuracy. Model compression techniques can be leveraged to efficiently deploy these compute-intensive ...

In the field of combinatorial optimisation, per-instance algorithm selection still remains a challenging problem, particularly with respect to streaming problems such as packing or scheduling. Typical approaches involve training a model to predict the ...

Recently it has been proved that a simple algorithm configurator called ParamRLS can efficiently identify the optimal neighbourhood size to be used by stochastic local search to optimise two standard benchmark problem classes. In this paper we analyse ...

In optimization and machine learning, the divide between discrete and continuous problems and methods is deep and persistent. We attempt to remove this distinction by training neural network autoencoders that embed discrete candidate solutions in ...

Recent progress in the runtime analysis of evolutionary algorithms (EAs) has allowed the derivation of upper bounds on the expected runtime of standard steady-state GAs. These upper bounds have shown speed-ups of the GAs using crossover and mutation ...

We applied a Biased Random Key Genetic Algorithm (BRKGA) to solve the Vehicle Routing Problem with Time Windows and Synchronization Constraints. Additionally, both vehicles and clients are skilled, and each client can require up to two distinct skills ...

We present a new method, Synthesis through Unification Genetic Programming (STUN GP), which synthesizes provably correct programs using a Divide and Conquer approach. This method first splits the input space by undergoing a discovery phase that uses ...

The application of Evolutionary Algorithms (EAs) to real-world problems comes with inherent challenges, primarily the difficulty in defining the large number of considerations needed when designing complex systems such as Water Distribution Networks (...

On the one hand, surrogate-assisted evolutionary algorithms are established as a method of choice for expensive black-box optimization problems. On the other hand, the growth in computing facilities has seen a massive increase in potential computational ...

The growing number of novel swarm-based meta-heuristics has been raising debates regarding their novelty. These algorithms often claim to be inspired by different concepts from nature but the proponents of these seldom demonstrate whether the novelty ...

The self-adjusting (1 + (λ, λ)) GA is the best known genetic algorithm for problems with a good fitness-distance correlation as in OneMax. It uses a parameter control mechanism for the parameter λ that governs the mutation strength and the number of ...

Real-world problems such as computational fluid dynamics simulations and finite element analyses are computationally expensive. A standard approach to mitigating the high computational expense is Surrogate-Based Optimization (SBO). Yet, due to the high-...

Transfer learning entails taking an artificial neural network (ANN) that is trained on a source dataset and adapting it to a new target dataset. While this has been shown to be quite powerful, its use has generally been restricted by architectural ...

In classification, feature selection mainly aims at reducing the dataset dimensionality and increasing the classification accuracy, which also results in higher computational efficiency than using the original full set of features. Population-based meta-...