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[1904.08658] Batch Tournament Selection for Genetic Programming
Lexicase selection achieves very good solution quality by introducing ordered test cases. However, the computational complexity of lexicase selection can prohibit its use in many applications. In this paper, we introduce Batch Tournament Selection (BTS), a hybrid of tournament and lexicase selection which is approximately one order of magnitude faster than lexicase selection while achieving a competitive quality of solutions. Tests on a number of regression datasets show that BTS compares well with lexicase selection in terms of mean absolute error while having a speed-up of up to 25 times. Surprisingly, BTS and lexicase selection have almost no difference in both diversity and performance. This reveals that batches and ordered test cases are completely different mechanisms which share the same general principle fostering the specialization of individuals. This work introduces an efficient algorithm that sheds light onto the main principles behind the success of lexicase, potentially opening up a new range of possibilities for algorithms to come.
genetic-programming  selection  algorithms  hey-I-know-this-guy  (I-wish-it-wasn't-always-a-guy)  to-write-about  to-do 
april 2019 by Vaguery
[1810.07800] Alignments as Compositional Structures
Alignments, i.e., position-wise comparisons of two or more strings or ordered lists are of utmost practical importance in computational biology and a host of other fields, including historical linguistics and emerging areas of research in the Digital Humanities. The problem is well-known to be computationally hard as soon as the number of input strings is not bounded. Due to its prac- tical importance, a huge number of heuristics have been devised, which have proved very successful in a wide range of applications. Alignments nevertheless have received hardly any attention as formal, mathematical structures. Here, we focus on the compositional aspects of alignments, which underlie most algo- rithmic approaches to computing alignments. We also show that the concepts naturally generalize to finite partially ordered sets and partial maps between them that in some sense preserve the partial orders.
discrete-mathematics  optimization  alignments  combinatorics  hey-I-know-this-guy  bioinformatics  rather-interesting  formalization  to-write-about  consider:multiobjective-optimization  consider:fitness-landscapes  question:transitivity 
april 2019 by Vaguery
Geometric semantic genetic programming for recursive boolean programs
Geometric Semantic Genetic Programming (GSGP) induces a unimodal fitness landscape for any problem that consists in finding a function fitting given input/output examples. Most of the work around GSGP to date has focused on real-world applications and on improving the originally proposed search operators, rather than on broadening its theoretical framework to new domains. We extend GSGP to recursive programs, a notoriously challenging domain with highly discontinuous fitness landscapes. We focus on programs that map variable-length Boolean lists to Boolean values, and design search operators that are provably efficient in the training phase and attain perfect generalization. Computational experiments complement the theory and demonstrate the superiority of the new operators to the conventional ones. This work provides new insights into the relations between program syntax and semantics, search operators and fitness landscapes, also for more general recursive domains.
genetic-programming  hey-I-know-this-guy  generative-programming  to-understand  to-write-about 
april 2019 by Vaguery
[1903.07008] Leveling the Playing Field -- Fairness in AI Versus Human Game Benchmarks
From the beginning if the history of AI, there has been interest in games as a platform of research. As the field developed, human-level competence in complex games became a target researchers worked to reach. Only relatively recently has this target been finally met for traditional tabletop games such as Backgammon, Chess and Go. Current research focus has shifted to electronic games, which provide unique challenges. As is often the case with AI research, these results are liable to be exaggerated or misrepresented by either authors or third parties. The extent to which these games benchmark consist of fair competition between human and AI is also a matter of debate. In this work, we review the statements made by authors and third parties in the general media and academic circle about these game benchmark results and discuss factors that can impact the perception of fairness in the contest between humans and machines
engineering-criticism  rather-interesting  hey-I-know-this-guy  performance-measure  what-gets-measured-gets-fudged  artificial-intelligence  games  machine-learning  to-write-about  benchmarking 
april 2019 by Vaguery
The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System | Artificial Life | MIT Press Journals
Many believe that an essential component for the discovery of the tremendous diversity in natural organisms was the evolution of evolvability, whereby evolution speeds up its ability to innovate by generating a more adaptive pool of offspring. One hypothesized mechanism for evolvability is developmental canalization, wherein certain dimensions of variation become more likely to be traversed and others are prevented from being explored (e.g., offspring tend to have similar-size legs, and mutations affect the length of both legs, not each leg individually). While ubiquitous in nature, canalization is rarely reported in computational simulations of evolution, which deprives us of in silico examples of canalization to study and raises the question of which conditions give rise to this form of evolvability. Answering this question would shed light on why such evolvability emerged naturally, and it could accelerate engineering efforts to harness evolution to solve important engineering challenges. In this article, we reveal a unique system in which canalization did emerge in computational evolution. We document that genomes entrench certain dimensions of variation that were frequently explored during their evolutionary history. The genetic representation of these organisms also evolved to be more modular and hierarchical than expected by chance, and we show that these organizational properties correlate with increased fitness. Interestingly, the type of computational evolutionary experiment that produced this evolvability was very different from traditional digital evolution in that there was no objective, suggesting that open-ended, divergent evolutionary processes may be necessary for the evolution of evolvability.
artificial-life  contingency  evolution  theoretical-biology  evolvability  to-write-about  hey-I-know-this-guy 
march 2019 by Vaguery
[1711.08477] Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining
Modern biomedical data mining requires feature selection methods that can (1) be applied to large scale feature spaces (e.g. `omics' data), (2) function in noisy problems, (3) detect complex patterns of association (e.g. gene-gene interactions), (4) be flexibly adapted to various problem domains and data types (e.g. genetic variants, gene expression, and clinical data) and (5) are computationally tractable. To that end, this work examines a set of filter-style feature selection algorithms inspired by the `Relief' algorithm, i.e. Relief-Based algorithms (RBAs). We implement and expand these RBAs in an open source framework called ReBATE (Relief-Based Algorithm Training Environment). We apply a comprehensive genetic simulation study comparing existing RBAs, a proposed RBA called MultiSURF, and other established feature selection methods, over a variety of problems. The results of this study (1) support the assertion that RBAs are particularly flexible, efficient, and powerful feature selection methods that differentiate relevant features having univariate, multivariate, epistatic, or heterogeneous associations, (2) confirm the efficacy of expansions for classification vs. regression, discrete vs. continuous features, missing data, multiple classes, or class imbalance, (3) identify previously unknown limitations of specific RBAs, and (4) suggest that while MultiSURF* performs best for explicitly identifying pure 2-way interactions, MultiSURF yields the most reliable feature selection performance across a wide range of problem types.
machine-learning  bioinformatics  hey-I-know-this-guy  feature-selection  benchmarking  epistasis  algorithms  to-write-about 
february 2019 by Vaguery
[1812.05225] Finding the origin of noise transients in LIGO data with machine learning
Quality improvement of interferometric data collected by gravitational-wave detectors such as Advanced LIGO and Virgo is mission critical for the success of gravitational-wave astrophysics. Gravitational-wave detectors are sensitive to a variety of disturbances of non-astrophysical origin with characteristic frequencies in the instrument band of sensitivity. Removing non-astrophysical artifacts that corrupt the data stream is crucial for increasing the number and statistical significance of gravitational-wave detections and enabling refined astrophysical interpretations of the data. Machine learning has proved to be a powerful tool for analysis of massive quantities of complex data in astronomy and related fields of study. We present two machine learning methods, based on random forest and genetic programming algorithms, that can be used to determine the origin of non-astrophysical transients in the LIGO detectors. We use two classes of transients with known instrumental origin that were identified during the first observing run of Advanced LIGO to show that the algorithms can successfully identify the origin of non-astrophysical transients in real interferometric data and thus assist in the mitigation of instrumental and environmental disturbances in gravitational-wave searches. While the data sets described in this paper are specific to LIGO, and the exact procedures employed were unique to the same, the random forest and genetic programming code bases and means by which they were applied as a dual machine learning approach are completely portable to any number of instruments in which noise is believed to be generated through mechanical couplings, the source of which is not yet discovered.
genetic-programming  hey-I-know-this-guy  astrophysics  data-analysis  data-mining  to-understand  feature-construction  classification 
january 2019 by Vaguery
What else is in an evolved name? Exploring evolvable specificity with SignalGP [PeerJ Preprints]
Tags are evolvable labels that provide genetic programs a flexible mechanism for specification. Tags are used to label and refer to programmatic elements, such as functions or jump targets. However, tags differ from traditional, more rigid methods for handling labeling because they allow for inexact references; that is, a referring tag need not exactly match its referent. Here, we explore how adjusting the threshold for how what qualifies as a match affects adaptive evolution. Further, we propose broadened applications of tags in the context of a genetic programming (GP) technique called SignalGP. SignalGP gives evolution direct access to the event-driven paradigm. Program modules in SignalGP are tagged and can be triggered by signals (with matching tags) from the environment, from other agents, or due to internal regulation. Specifically, we propose to extend this tag based system to: (1) provide more fine-grained control over module execution and regulation (e.g., promotion and repression) akin to natural gene regulatory networks, (2) employ a mosaic of GP representations within a single program, and (3) facilitate major evolutionary transitions in individuality (i.e., allow hierarchical program organization to evolve de novo).
artificial-life  genetic-programming  representation  hey-I-know-this-guy  the-mangle-in-practice  to-examine  consider:`ReQ` 
january 2019 by Vaguery
Accelerating open source LLVM development - Software Tools blog - Software Tools - Arm Community
We are currently using Works on Arm as the underlying platform to run these build jobs. Works on Arm provides free-of-charge Arm-based infrastructure for open source projects. In that way, we gain access to powerful build servers to complete such a challenging task within acceptable build time constraints.
ARM  to-write-about  hey-I-know-this-guy  opensource  resources  to-do 
january 2019 by Vaguery
[quant-ph/0208149] A semi-quantum version of the game of Life
A version of John Conway's game of Life is presented where the normal binary values of the cells are replaced by oscillators which can represent a superposition of states. The original game of Life is reproduced in the classical limit, but in general additional properties not seen in the original game are present that display some of the effects of a quantum mechanical Life. In particular, interference effects are seen.
quantums  Game-of-Life  hey-I-know-this-guy  cellular-automata 
november 2018 by Vaguery
Semantic information, agency, & physics | Interface Focus
Shannon information theory provides various measures of so-called syntactic information, which reflect the amount of statistical correlation between systems. By contrast, the concept of ‘semantic information’ refers to those correlations which carry significance or ‘meaning’ for a given system. Semantic information plays an important role in many fields, including biology, cognitive science and philosophy, and there has been a long-standing interest in formulating a broadly applicable and formal theory of semantic information. In this paper, we introduce such a theory. We define semantic information as the syntactic information that a physical system has about its environment which is causally necessary for the system to maintain its own existence. ‘Causal necessity’ is defined in terms of counter-factual interventions which scramble correlations between the system and its environment, while ‘maintaining existence’ is defined in terms of the system's ability to keep itself in a low entropy state. We also use recent results in non-equilibrium statistical physics to analyse semantic information from a thermodynamic point of view. Our framework is grounded in the intrinsic dynamics of a system coupled to an environment, and is applicable to any physical system, living or otherwise. It leads to formal definitions of several concepts that have been intuitively understood to be related to semantic information, including ‘value of information’, ‘semantic content’ and ‘agency’.
complexity  philosophy-of-science  information-theory  define-your-terms  hey-I-know-this-guy  semantics  to-understand  cannot-read 
november 2018 by Vaguery
Adam Kotsko The Political Theology of Neoliberalism - state of nature
Neoliberals do rely on libertarian rhetoric, but libertarianism is basically neoliberalism for fools. When neoliberals are talking amongst themselves, they always acknowledge that a strong state is absolutely necessary to their agenda. This is because markets do not spontaneously arise in the absence of state interference, or in other words, markets are not natural. They must be artificially constructed, and so one way of defining neoliberalism is as a project to use state power to cultivate or create markets so that people will be forced to be free in the neoliberal sense.
neoliberalism  interview  quotes  hey-I-know-this-guy  to-write-about  fascism  political-economy  financial-crisis  capitalism  worldview 
october 2018 by Vaguery
[1806.01387] New And Surprising Ways to Be Mean. Adversarial NPCs with Coupled Empowerment Minimisation
Creating Non-Player Characters (NPCs) that can react robustly to unforeseen player behaviour or novel game content is difficult and time-consuming. This hinders the design of believable characters, and the inclusion of NPCs in games that rely heavily on procedural content generation. We have previously addressed this challenge by means of empowerment, a model of intrinsic motivation, and demonstrated how a coupled empowerment maximisation (CEM) policy can yield generic, companion-like behaviour. In this paper, we extend the CEM framework with a minimisation policy to give rise to adversarial behaviour. We conduct a qualitative, exploratory study in a dungeon-crawler game, demonstrating that CEM can exploit the affordances of different content facets in adaptive adversarial behaviour without modifications to the policy. Changes to the level design, underlying mechanics and our character's actions do not threaten our NPC's robustness, but yield new and surprising ways to be mean.
hey-I-know-this-guy  coevolution  evolutionary-algorithms  engineering-design  rather-interesting  to-write-about 
june 2018 by Vaguery
[1806.02717] Gamorithm
Examining games from a fresh perspective we present the idea of game-inspired and game-based algorithms, dubbed "gamorithms".
hey-I-know-this-guy  machine-learning  philosophy-of-engineering  representation  to-write-about 
june 2018 by Vaguery
Home | Kappa Language
By separating a rule from a patch on which it acts we gain a much clearer approach to mechanistic causality. If causal analysis were to proceed at the level of patches, it would obfuscate the causal structure of a system by dragging along context irrelevant to an event. In addition to simulation and static analysis, the Kappa platform also extracts the causal structure of a rule system from its simulation traces.
bioinformatics  representation  hey-I-know-this-guy  complexology  pattern-discovery  rather-interesting  to-write-about 
may 2018 by Vaguery
Trent McConaghy - FFX
FFX is a technique for symbolic regression, to induce whitebox models given X/y training data. It does Fast Function Extraction. It is:

Fast - runtime 5-60 seconds, depending on problem size (1GHz cpu)
Scalable - 1000 input variables, no problem!
Deterministic - no need to "hope and pray".
If you ignore the whitebox-model aspect, FFX can be viewed as a regression tool. It's been used this way for thousands of industrial problems with 100K+ input variables. It can also be used as a classifier (FFXC), by wrapping the output with a logistic map. This has also been used successfully on thousands of industrial problems.
hey-I-know-this-guy  symbolic-regression  algorithms  numerical-methods  data-analysis  to-write-about 
may 2018 by Vaguery
[1804.05445] Evolving Event-driven Programs with SignalGP
We present SignalGP, a new genetic programming (GP) technique designed to incorporate the event-driven programming paradigm into computational evolution's toolbox. Event-driven programming is a software design philosophy that simplifies the development of reactive programs by automatically triggering program modules (event-handlers) in response to external events, such as signals from the environment or messages from other programs. SignalGP incorporates these concepts by extending existing tag-based referencing techniques into an event-driven context. Both events and functions are labeled with evolvable tags; when an event occurs, the function with the closest matching tag is triggered. In this work, we apply SignalGP in the context of linear GP. We demonstrate the value of the event-driven paradigm using two distinct test problems (an environment coordination problem and a distributed leader election problem) by comparing SignalGP to variants that are otherwise identical, but must actively use sensors to process events or messages. In each of these problems, rapid interaction with the environment or other agents is critical for maximizing fitness. We also discuss ways in which SignalGP can be generalized beyond our linear GP implementation.
gptp  hey-I-know-this-guy  genetic-programming  representation  to-write-about 
may 2018 by Vaguery
[1803.05859v3] Neural Network Quine
Self-replication is a key aspect of biological life that has been largely overlooked in Artificial Intelligence systems. Here we describe how to build and train self-replicating neural networks. The network replicates itself by learning to output its own weights. The network is designed using a loss function that can be optimized with either gradient-based or non-gradient-based methods. We also describe a method we call regeneration to train the network without explicit optimization, by injecting the network with predictions of its own parameters. The best solution for a self-replicating network was found by alternating between regeneration and optimization steps. Finally, we describe a design for a self-replicating neural network that can solve an auxiliary task such as MNIST image classification. We observe that there is a trade-off between the network's ability to classify images and its ability to replicate, but training is biased towards increasing its specialization at image classification at the expense of replication. This is analogous to the trade-off between reproduction and other tasks observed in nature. We suggest that a self-replication mechanism for artificial intelligence is useful because it introduces the possibility of continual improvement through natural selection.
artificial-life  machine-learning  quines  rather-interesting  to-write-about  hey-I-know-this-guy 
may 2018 by Vaguery
Complexity Explorer
New for 2018, our flagship course, Introduction to Complexity, will be open year round. All units will be available at all times, so you can learn the fundamentals of Complex Systems Science at your own pace, and earn your certificate at any time. 

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MOOC  complexology  hey-I-know-this-guy  to-watch  to-write-about 
april 2018 by Vaguery

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