Decomposition-based algorithms have emerged as one of the most popular classes of solvers for multi-objective optimization. Despite their popularity, a lack of guidance exists for how to configure such algorithms for real-world problems, based on the ...

When working with decomposition-based algorithms, an appropriate set of weights might improve quality of the final solution. A set of uniformly distributed weights usually leads to well-distributed solutions on a Pareto front. However, there are two main ...

In the past, evolutionary algorithms (EAs) that use probabilistic modeling of the best solutions incorporated latent or hidden variables to the models as a more accurate way to represent the search distributions. Recently, a number of neural-network ...

The redundant mapping from genotype to phenotype is common in evolutionary algorithms, where multiple genotypes can map to the same phenotype. Such a redundancy has been suggested to make an evolutionary system robust as well as evolvable. However, the ...

This paper studies the performance for evolution strategies with the optimal weighed recombination on spherical problems in finite dimensions. We first discuss the different forms of functions that are used to derive the optimal recombination weights and ...

When search operators in genetic programming (GP) insert new instructions into programs, they usually draw them uniformly from the available instruction set. Prefering some instructions to others would require additional domain knowledge, which is ...

The mutant vector generation strategy is an essential component of Differential Evolution (de), introduced to promote diversity, resulting in exploration of novel areas of the search space. However, it is also responsible for promoting intensification, ...

Cooperative co-evolution (CC) is an effective framework that can be used to solve large-scale optimization problems. It typically divides a problem into components and uses one optimizer to solve the components in a round-robin fashion. However the ...

We conduct a first fundamental analysis of the working principles of binary differential evolution (BDE), an optimization heuristic for binary decision variables that was derived by Gong and Tuson (2007) from the very successful classic differential ...

Theoretical and empirical research on evolutionary computation methods complement each other by providing two fundamentally different approaches towards a better understanding of black-box optimization heuristics. In discrete optimization, both streams ...

We investigate solving of Multi-Mode Resource-Constrained Multiple Projects Scheduling Problem by heuristic techniques. A new method based on Min-Conflicts heuristic is proposed and evaluated. The main idea is to efficiently explore the neighborhood of ...

Insider threat detection represents a challenging problem to companies and organizations where malicious actions are performed by authorized users. This is a highly skewed data problem, where the huge class imbalance makes the adaptation of learning ...

In multiobjective optimization, many techniques are used to visualize the results, ranging from traditional general-purpose data visualization techniques to approaches tailored to the specificities of multiobjective optimization. The number of ...

Evolution has produced an astonishing diversity of species, each filling a different niche. Algorithms like MAP-Elites mimic this divergent evolutionary process to find a set of behaviorally diverse but high-performing solutions, called the elites. Our ...

We propose Neural Estimation of Interaction Outcomes (NEIO), a method that reduces the number of required interactions between candidate solutions and tests in test-based problems. Given the outcomes of a random sample of all solution-test interactions, ...

In genetic programming (GP), population diversity represents a key aspect of evolutionary search and a major factor in algorithm performance. In this paper we propose a new schema-based approach for observing and steering the loss of diversity in GP ...

The availability of several CPU cores on current computers enables parallelization and increases the computational power significantly. Optimization algorithms have to be adapted to exploit these highly parallelized systems and evaluate multiple ...

Algorithm selection is useful in decision situations where among many alternative algorithm instances one has to be chosen. This is often the case in heuristic optimization and is detailed by the well-known no-free-lunch (NFL) theorem. A consequence of ...

Estimation of distribution programming (EDP) replaces standard GP variation operators with sampling from a learned probability model. To ensure a minimum amount of variation in a population, EDP adds random noise to the probabilities of random variables. ...

In the theory community of evolutionary computation, linear pseudo-boolean functions are often regarded as easy problems since all of them can be optimized in expected time O(n log n) by simple unbiased algorithms. However, results from genetic ...