To investigate the consequences of hybridization between species, we studied three replicate hybrid populations that formed naturally between two swordtail fish species, estimating their fine-scale genetic map and inferring ancestry along the genomes of 690 individuals. In all three populations, ancestry from the "minor" parental species is more common in regions of high recombination and where there is linkage to fewer putative targets of selection. The same patterns are apparent in a reanalysis of human and archaic admixture. These results support models in which ancestry from the minor parental species is more likely to persist when rapidly uncoupled from alleles that are deleterious in hybrids. Our analyses further indicate that selection on swordtail hybrids stems predominantly from deleterious combinations of epistatically interacting alleles.

When bees leave their hive hoping to find a better location for their nest, they often first settle nearby, usually on a tree branch, and cluster around the queen while several dozen scouts go off in search of a new home. Each scout then returns and starts to dance, indicating the direction and distance of the site it found. The more excited they become, the more frantically they dance, signaling the others to have a look. Ultimately, a favorite location emerges from all this swarming and buzzing about -- and they all depart and fly to it. In computer science, this behavior is known as particle swarm optimization, which holds that each particle's movement not only is influenced by its own position but is guided to other good positions, all of which are updated as other particles find better positions.

Sudoku is a well-known logic-based puzzle game that was first published in 1979 under the name of "Number Place". It was popularised in Japan in 1984 by the puzzle company Nikoli, and later named "Sudoku", which roughly translates to "single digits". The puzzle gained attention in the West in 2004, after The Times published its first Sudoku grid (at the instigation of Hong Kong-based judge Wayne Gould, who first encountered the puzzle in 1997, and developed a computer program to automatically generate instances). Sudoku is now a global phenomenon, and many newspapers now carry it alongside their existing crosswords (see [4] for a general history of the puzzle). The simplest variant of Sudoku uses a 9 9 grid of cells divided into nine 3 3 subgrids (Figure 1 (left)). The aim of the puzzle is to fill the grid with digits such that each row, each column, and each 3 3 subgrid contains all of the digits 1-9 (Figure 1 (right)). An instance of Sudoku provides, at the outset, a partially-completed grid, but the difficulty of any grid derives more from the range of techniques required to solve it than the number of cell values that are provided for the player. Sudoku is an NPcomplete problem [12], as first shown in [35] (via a reduction from the Latin Square Completion problem [2]).

This paper describes the N-Tuple Bandit Evolutionary Algorithm (NTBEA) and its application to optimising the parameters of a rolling horizon evolution game-playing agent. The NTBEA combines evolutionary search with Multi-Armed Bandit algorithms (MABs) in order to provide an algorithm which is robust to noise, has an explicit way to balance the tradeoff between exploration and exploitation, and provides a statistical model of the fitness landscape as an additional output. The applications of this type of algorithm are numerous. In our research we have already applied it successfully to hyperparameter optimisation [1] and automated game tuning [2]. Furthermore, if the inherent fitness landscape is flat, then the exploration term provides a means for performing novelty search [3]. A. Estimation of Distribution Algorithms Estimation of Distribution Algorithms (EDAs) [4], [5], [6], [7], [8] are a powerful class of Evolutionary Algorithms (EAs).

This paper presents a novel Differential Evolution algorithm for protein folding optimization that is applied to a three-dimensional AB off-lattice model. The proposed algorithm includes two new mechanisms. A local search is used to improve convergence speed and to reduce the runtime complexity of the energy calculation. For this purpose, a local movement is introduced within the local search. The designed evolutionary algorithm has fast convergence speed and, therefore, when it is trapped into the local optimum or a relatively good solution is located, it is hard to locate a better similar solution. The similar solution is different from the good solution in only a few components. A component reinitialization method is designed to mitigate this problem. Both the new mechanisms and the proposed algorithm were analyzed on well-known amino acid sequences that are used frequently in the literature. Experimental results show that the employed new mechanisms improve the efficiency of our algorithm and that the proposed algorithm is superior to other state-of-the-art algorithms. It obtained a hit ratio of 100% for sequences up to 18 monomers, within a budget of $10^{11}$ solution evaluations. New best-known solutions were obtained for most of the sequences. The existence of the symmetric best-known solutions is also demonstrated in the paper.

Recent advances in deep neuroevolution have demonstrated that evolutionary algorithms, such as evolution strategies (ES) and genetic algorithms (GA), can scale to train deep neural networks to solve difficult reinforcement learning (RL) problems. However, it remains a challenge to analyze and interpret the underlying process of neuroevolution in such high dimensions. To begin to address this challenge, this paper presents an interactive data visualization tool called VINE (Visual Inspector for NeuroEvolution) aimed at helping neuroevolution researchers and end-users better understand and explore this family of algorithms. VINE works seamlessly with a breadth of neuroevolution algorithms, including ES and GA, and addresses the difficulty of observing the underlying dynamics of the learning process through an interactive visualization of the evolving agent's behavior characterizations over generations. As neuroevolution scales to neural networks with millions or more connections, visualization tools like VINE that offer fresh insight into the underlying dynamics of evolution become increasingly valuable and important for inspiring new innovations and applications.

Generative Adversarial Networks (GANs) are a machine learning approach capable of generating novel example outputs across a space of provided training examples. Procedural Content Generation (PCG) of levels for video games could benefit from such models, especially for games where there is a pre-existing corpus of levels to emulate. This paper trains a GAN to generate levels for Super Mario Bros using a level from the Video Game Level Corpus. The approach successfully generates a variety of levels similar to one in the original corpus, but is further improved by application of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Specifically, various fitness functions are used to discover levels within the latent space of the GAN that maximize desired properties. Simple static properties are optimized, such as a given distribution of tile types. Additionally, the champion A* agent from the 2009 Mario AI competition is used to assess whether a level is playable, and how many jumping actions are required to beat it. These fitness functions allow for the discovery of levels that exist within the space of examples designed by experts, and also guide the search towards levels that fulfill one or more specified objectives.

Organisms result from adaptive processes interacting across different time scales. One such interaction is that between development and evolution. Models have shown that development sweeps over several traits in a single agent, sometimes exposing promising static traits. Subsequent evolution can then canalize these rare traits. Thus, development can, under the right conditions, increase evolvability. Here, we report on a previously unknown phenomenon when embodied agents are allowed to develop and evolve: Evolution discovers body plans robust to control changes, these body plans become genetically assimilated, yet controllers for these agents are not assimilated. This allows evolution to continue climbing fitness gradients by tinkering with the developmental programs for controllers within these permissive body plans. This exposes a previously unknown detail about the Baldwin effect: instead of all useful traits becoming genetically assimilated, only traits that render the agent robust to changes in other traits become assimilated. We refer to this as differential canalization. This finding also has implications for the evolutionary design of artificial and embodied agents such as robots: robots robust to internal changes in their controllers may also be robust to external changes in their environment, such as transferal from simulation to reality or deployment in novel environments.

An evolution strategy (ES) variant based on a simplification of a natural evolution strategy recently attracted attention because it performs surprisingly well in challenging deep reinforcement learning domains. It searches for neural network parameters by generating perturbations to the current set of parameters, checking their performance, and moving in the aggregate direction of higher reward. Because it resembles a traditional finite-difference approximation of the reward gradient, it can naturally be confused with one. However, this ES optimizes for a different gradient than just reward: It optimizes for the average reward of the entire population, thereby seeking parameters that are robust to perturbation. This difference can channel ES into distinct areas of the search space relative to gradient descent, and also consequently to networks with distinct properties. This unique robustness-seeking property, and its consequences for optimization, are demonstrated in several domains. They include humanoid locomotion, where networks from policy gradient-based reinforcement learning are significantly less robust to parameter perturbation than ES-based policies solving the same task. While the implications of such robustness and robustness-seeking remain open to further study, this work's main contribution is to highlight such differences and their potential importance.

Journal of Parallel and Distributed Computing 104, 19-35.