An ADMM Based Framework for AutoML Pipeline Configuration

We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines. This black-box (gradient-free) optimization with mixed integer & continuous variables is a challenging problem. We propose a novel AutoML scheme by leveraging the alternating direction method of multipliers (ADMM). The proposed framework is able to (i) decompose the optimization problem into easier sub-problems that have a reduced number of variables and circumvent the challenge of mixed variable categories, and (ii) incorporate black-box constraints along-side the black-box optimization objective. We empirically evaluate the flexibility (in utilizing existing AutoML techniques), effectiveness (against open source AutoML toolkits),and unique capability (of executing AutoML with practically motivated black-box constraints) of our proposed scheme on a collection of binary classification data sets from UCI ML& OpenML repositories. We observe that on an average our framework provides significant gains in comparison to other AutoML frameworks (Auto-sklearn & TPOT), highlighting the practical advantages of this framework

Gene Regulatory Network Evolution Through Augmenting Topologies

International audienceArtificial gene regulatory networks (GRNs) are biologically inspired dynamical systems used to control various kinds of agents, from the cells in developmental models to embodied robot swarms. Most recent work uses a genetic algorithm (GA) or an evolution strategy in order to optimize the network for a specific task. However, the empirical performances of these algorithms are unsatisfactory. This paper presents an algorithm that primarily exploits a network distance metric, which allows genetic similarity to be used for speciation and variation of GRNs. This algorithm, inspired by the successful neuroevolution of augmenting topologies algorithm's use in evolving neural networks and compositional pattern-producing networks, is based on a specific initialization method, a crossover operator based on gene alignment, and speciation based upon GRN structures. We demonstrate the effectiveness of this new algorithm by comparing our approach both to a standard GA and to evolutionary programming on four different experiments from three distinct problem domains, where the proposed algorithm excels on all experiments

Simulated Experince Evaluation in Developing Multi-agent Coordination Graphs

Cognitive science has proposed that a way people learn is through self-critiquing by generating \u27what-if\u27 strategies for events (simulation). It is theorized that people use this method to learn something new as well as to learn more quickly. This research adds this concept to a graph-based genetic program. Memories are recorded during fitness assessment and retained in a global memory bank based on the magnitude of change in the agent’s energy and age of the memory. Between generations, candidate agents perform in simulations of the stored memories. Candidates that perform similarly to good memories and differently from bad memories are more likely to be included in the next generation. The simulation-informed genetic program is evaluated in two domains: sequence matching and Robocode. Results indicate the algorithm does not perform equally in all environments. In sequence matching, experiential evaluation fails to perform better than the control. However, in Robocode, the experiential evaluation method initially outperforms the control then stagnates and often regresses. This is likely an indication that the algorithm is over-learning a single solution rather than adapting to the environment and that learning through simulation includes a satisficing component

Word Blending and Other Formal Models of Bio-operations

As part of ongoing efforts to view biological processes as computations, several formal models of DNA-based processes have been proposed and studied in the formal language literature. In this thesis, we survey some classical formal language word and language operations, as well as several bio-operations, and we propose a new operation inspired by a DNA recombination lab protocol known as Cross-pairing Polymerase Chain Reaction, or XPCR. More precisely, we define and study a word operation called word blending which models a special case of XPCR, where two words x w p and q w y sharing a non-empty overlap part w generate the word x w y. Properties of word blending that we study include closure properties of the Chomsky families of languages under this operation and its iterated version, existence of solution to equations involving this operation, and its state complexity

Computer Music Algorithms. Bio-inspired and Artificial Intelligence Applications

2014 - 2015Music is one of the arts that have most benefited from the invention of computers. Originally, the term Computer Music was used in the scientific community to identify the application of information technology in music composition. It began over time to include the theory and application of new or existing technologies in music, such as sound synthesis, sound design, acoustic, psychoacoustic. Thanks to its interdisciplinary nature, Computer Music can be seen as the encounter of different disciplines. In the last years technology has redefined the way individuals can work, communicate, share experiences, constructively debate, and actively participate to any aspect of the daily life, ranging from business to education, from political and intellectual to social, and also in music activity, such as play music, compose music and so on. In this new context, Computer Music has become an emerging research area for the application of Computational Intelligence techniques, such as machine learning, pattern recognition, bio-inspired algorithms and so on. My research activity is concerned with the Bio-inspired and Artificial Intelligence Applications in the Computer Music. Some of the problems I addressed are summarized in the following. Automatic composition of background music for games, films and other human activities: EvoBackMusic. Systems for real-time composition of background music respond to changes of the environment by generating music that matches the current state of the environment and/or of the user. We propose one such a system that we call EvoBackMusic. It is a multiagent system that exploits a feed-forward neural network and a multi-objective genetic algorithm to produce background music. The neural network is trained to learn the preferences of the user and such preferences are exploited by the genetic algorithm to compose the music. The composition process takes into account a set of controllers that describe several aspects of the environment, like the dynamism of both the user and the 2 context, other physical characteristics, and the emotional state of the user. Previous system mainly focus on the emotional aspect. Publications: • Roberto De Prisco, Delfina Malandrino, Gianluca Zaccagnino, Rocco Zaccagnino: ‘‘An Evolutionary Composer for Real-Time Background Music’’. EvoMUSART 2016: 135-151. Interaction modalities for music performances: MarcoSmiles. In this field we considered new interaction modalities during music performances by using hands without the support of a real musical instrument. Exploiting natural user interfaces (NUI), initially conceived for the game market, it is possible to enhance the traditional modalities of interaction when accessing to technology, build new forms of interactions by transporting users in a virtual dimension, but that fully reflects the reality, and finally, improve the overall perceived experience. The increasing popularity of these innovative interfaces involved their adoption in other fields, including Computer Music. We propose a system, named MarcoSmiles, specifically designed to allow individuals to perform music in an easy, innovative, and personalized way. The idea is to design new interaction modalities during music performances by using hands without the support of a real musical instrument. We exploited Artificial Neural Networks to customize the virtual musical instrument, to provide the information for the mapping of the hands configurations into musical notes and, finally, to train and test these configurations. We performed several tests to study the behavior of the system and its efficacy in terms of learning capabilities. Publications: • Roberto De Prisco, Delfina Malandrino, Gianluca Zaccagnino, Rocco Zaccagnino: ‘‘Natural Users Interfaces to support and enhance Real-Time Music Performance’’. AVI 2016. 3 Bio-inspired approach for automatic music composition Here we describe a new bio-inspired approach for automatic music composition in a specific style: Music Splicing System. Splicing systems were introduced by Tom Head (1987) as a formal model of a recombination process between DNA molecules. The existing literature on splicing systems mainly focuses on the computational power of these systems and on the properties of the generated languages; very few applications based on splicing systems have been introduced. We show a novel application of splicing systems to build an automatic music composer. As a result of a performance study we proved that our composer outperforms other meta-heuristics by producing better music according to a specific measure of quality evaluation, and this proved that the proposed system can be seen also as a new valid bio-inspired strategy for automatic music composition. Publications: ▪ Clelia De Felice, Roberto De Prisco, Delfina Malandrino, Gianluca Zaccagnino, Rocco Zaccagnino, Rosalba Zizza: ‘‘Splicing Music Composition’’. Information Sciences Journal, 385: 196 – 215 (2017). ▪ Clelia De Felice, Roberto De Prisco, Delfina Malandrino, Gianluca Zaccagnino, Rocco Zaccagnino, Rosalba Zizza: ‘‘Chorale Music Splicing System: An Algorithmic Music Composer Inspired by Molecular Splicing’’. EvoMusart 2015: 50 – 61. Music and Visualization Here we describe new approaches for learning of harmonic and melodic rules of classic music, by using visualization techniques: VisualMelody and VisualHarmony. Experienced musicians have the ability to understand the structural elements of music compositions. Such an ability is built over time through the study of music theory, the understanding of rules that guide the composition of music, and through countless hours of practice. The learning process is hard, especially for classical music, where the rigidity of the music structures and styles requires great effort to understand, assimilate, and then master the learned notions. In particular, we focused our attention on a specific type of music compositions, namely, music in chorale style (4-voice music). Composing such type of music 4 is often perceived as a difficult task, because of the rules the composer has to adhere to. In this paper we propose a visualization technique that can help people lacking a strong knowledge of music theory. The technique exploits graphic elements to draw the attention on the possible errors in the composition. We then developed two interactive systems, named VisualMelody and VisualHarmony, that employ the proposed visualization techniques to facilitate the understanding of the structure of music compositions. The aim is to allow people to make 4-voice music composition in a quick and effective way, i.e., avoiding errors, as dictated by classical music theory rules. Publications: ▪ Roberto De Prisco, Delfina Malandrino, Donato Pirozzi, Gianluca Zaccagnino, Rocco Zaccagnino: ‘‘Understanding the structure of music compositions: is visualization an effective approach?’’ Information Visualization Journal, 2016. (DOI): 10.1177/1473871616655468 • Delfina Malandrino, Donato Pirozzi, Gianluca Zaccagnino, Rocco Zaccagnino: ‘‘A Color-Based Visualization Approach to Understand Harmonic Structures of Musical Compositions’’. IV 2015: 56-61. • Delfina Malandrino, Donato Pirozzi, Gianluca Zaccagnino, Rocco Zaccagnino: ‘‘Visual Approaches for Harmonic Analysis of 4-part Music: Implementation and Evaluation’’. Major revision – Journal of Visual Languages and Computing, 2016. [edited by Author]XIV n.s