Reachability Analysis for Lexicase Selection via Community Assembly Graphs

Fitness landscapes have historically been a powerful tool for analyzing the search space explored by evolutionary algorithms. In particular, they facilitate understanding how easily reachable an optimal solution is from a given starting point. However, simple fitness landscapes are inappropriate for analyzing the search space seen by selection schemes like lexicase selection in which the outcome of selection depends heavily on the current contents of the population (i.e. selection schemes with complex ecological dynamics). Here, we propose borrowing a tool from ecology to solve this problem: community assembly graphs. We demonstrate a simple proof-of-concept for this approach on an NK Landscape where we have perfect information. We then demonstrate that this approach can be successfully applied to a complex genetic programming problem. While further research is necessary to understand how to best use this tool, we believe it will be a valuable addition to our toolkit and facilitate analyses that were previously impossible

Matchmaker, Matchmaker, Make Me a Match: Geometric, Variational, and Evolutionary Implications of Criteria for Tag Affinity

Genetic programming and artificial life systems commonly employ tag-matching schemes to determine interactions between model components. However, the implications of criteria used to determine affinity between tags with respect to constraints on emergent connectivity, canalization of changes to connectivity under mutation, and evolutionary dynamics have not been considered. We highlight differences between tag-matching criteria with respect to geometric constraint and variation generated under mutation. We find that tag-matching criteria can influence the rate of adaptive evolution and the quality of evolved solutions. Better understanding of the geometric, variational, and evolutionary properties of tag-matching criteria will facilitate more effective incorporation of tag matching into genetic programming and artificial life systems. By showing that tag-matching criteria influence connectivity patterns and evolutionary dynamics, our findings also raise fundamental questions about the properties of tag-matching systems in nature

Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented

Collection of Accountancy Case Studies

The following collection of case studies examines various personal and professional topics in accounting. These topics range from personal research and reflection on important topics within the profession to a comprehensive case competition focusing on The Coca-Cola Company. Throughout the collection, theoretical accounting frameworks and solutions are applied to real world scenarios. In addition to theoretical frameworks, financial statements and relevant outside sources are used with applicable

A suite of diagnostic metrics for characterizing selection schemes

Evolutionary algorithms are effective general-purpose techniques for solving optimization problems. Understanding how each component of an evolutionary algorithm influences its problem-solving success improves our ability to target particular problem domains. Our work focuses on evaluating selection schemes, which choose individuals to contribute genetic material to the next generation. We introduce four diagnostic search spaces for testing the strengths and weaknesses of selection schemes: the exploitation rate diagnostic, ordered exploitation rate diagnostic, contradictory objectives diagnostic, and the multi-path exploration diagnostic. Each diagnostic is handcrafted to isolate and measure the relative exploitation and exploration characteristics of selection schemes. In this study, we use our diagnostics to evaluate six population selection methods: truncation selection, tournament selection, fitness sharing, lexicase selection, nondominated sorting, and novelty search. Expectedly, tournament and truncation selection excelled in gradient exploitation but poorly explored search spaces, and novelty search excelled at exploration but failed to exploit fitness gradients. Fitness sharing performed poorly across all diagnostics, suggesting poor overall exploitation and exploration abilities. Nondominated sorting was best for maintaining populations comprised of individuals with different trade-offs of multiple objectives, but struggled to effectively exploit fitness gradients. Lexicase selection balanced search space exploration with exploitation, generally performing well across diagnostics. Our work demonstrates the value of diagnostic search spaces for building a deeper understanding of selection schemes, which can then be used to improve or develop new selection methods

Data Standards for Artificial Life Software

International audienc

MergeBathy (2015)

Developed in C++, MergeBathy (2015) is cross-platform and multi-threaded software suite for constructing digital bathymetric models. It provides the user with a set of modeling tools to construct custom bathymetric surfaces, including splines-in-tension routines for interpolation output or as an intermediate resampling step when merging multiple bathymetry data sets. Notable to MergeBathy is its user-friendly and flexible processing options made possible from its integrated bathymetric process framework. Keywords: MergeBathy, Bathymetry, Digital elevation models, Software, Tool, Modelin