Theory and practice of population diversity in evolutionary computation

Divergence of character is a cornerstone of natural evolution. On the contrary, evolutionary optimization processes are plagued by an endemic lack of population diversity: all candidate solutions eventually crowd the very same areas in the search space. The problem is usually labeled with the oxymoron “premature convergence” and has very different consequences on the different applications, almost all deleterious. At the same time, case studies from theoretical runtime analyses irrefutably demonstrate the benefits of diversity. This tutorial will give an introduction into the area of “diversity promotion”: we will define the term “diversity” in the context of Evolutionary Computation, showing how practitioners tried, with mixed results, to promote it. Then, we will analyze the benefits brought by population diversity in specific contexts, namely global exploration and enhancing the power of crossover. To this end, we will survey recent results from rigorous runtime analysis on selected problems. The presented analyses rigorously quantify the performance of evolutionary algorithms in the light of population diversity, laying the foundation for a rigorous understanding of how search dynamics are affected by the presence or absence of diversity and the introduction of diversity mechanisms

Evolutionary algorithms and machine learning: Synergies, Challenges and Opportunities

While Machine Learning (ML) techniques enjoyed growing popularity in recent years, the role of Evolutionary Algorithms in this field is still marginal — quite a surprising fact considering how deeply the origins of the two fields are related. In this tutorial we present success stories of EAs exploited in specific ML tasks, such as feature selection, adversarial ML, whitebox modeling, also mentioning the renowned neuroevolution. We show how similar concepts appear in both fields with different names. At the same time, we show well-known and emerging challenges that EAs need to overcome to become widely adopted in ML. For instance, a reduced ability to scale or a general distrust toward stochasticity. Finally, we point out opportunities arising for new research lines, that play on the strengths of EAs, such as potential improvements over currently-used optimization techniques; and the capability to go beyond simple model fitting, creating solutions that expand over the boundaries of the training data

Smart techniques for flying-probe testing

In the production of printed circuit boards, in-circuit tests verify whether the electric and electronic components of the board have been correctly soldered. When the test is performed using flying-probes, several probes are simultaneously moved on the board to reach and touch multiple test points. Taking into consideration the layout of the board, the characteristics of the tester, and several other physical constraints, not all movements of the probes are mutually compatible nor they can always be performed through simple straight lines. As the cost of the test is mainly related to its length, and patching the path of one probe may create new incompatibilities with the trajectory of the other probes, one should carefully trade off the time required to find the trajectories with the time required by the probes to follow them. In this paper, we model the movements of our flying probes as a multiple and collaborative planning problem. We describe an approach for detecting invalid movements and we design a strategy to correct them with the addition of new intermediate points in the trajectory. We report the entire high-level procedure and we explore the optimizations performed in the more expensive and complex steps. We also present parallel implementations of our algorithms, either relying on multi-core CPU devices or many-cores GPU platforms, when these units may be useful to achieve greater speedups. Experimental results show the effectiveness of the proposed solution in terms of elapsed computation times

Increasing pattern recognition accuracy for chemical sensing by evolutionary based drift compensation

Artificial olfaction systems, which mimic human olfaction by using arrays of gas chemical sensors combined with pattern recognition methods, represent a potentially low-cost tool in many areas of industry such as perfumery, food and drink production, clinical diagnosis, health and safety, environmental monitoring and process control. However, successful applications of these systems are still largely limited to specialized laboratories. Sensor drift, i.e., the lack of a sensor's stability over time, still limits real in dustrial setups. This paper presents and discusses an evolutionary based adaptive drift-correction method designed to work with state-of-the-art classification systems. The proposed approach exploits a cutting-edge evolutionary strategy to iteratively tweak the coefficients of a linear transformation which can transparently correct raw sensors' measures thus mitigating the negative effects of the drift. The method learns the optimal correction strategy without the use of models or other hypotheses on the behavior of the physical chemical sensors

Evolution-guided Engineering of Alpha/Beta Hydrolases

University of Minnesota Ph.D. dissertation. June 2017. Major: Biochemistry, Molecular Bio, and Biophysics. Advisor: Romas Kazlauskas. 1 computer file (PDF); xx, 321 pages.This work applies principles from evolution to engineering enzyme properties. Specifically, by examining the phylogeny and evolved sequence diversity in a group of α/β-hydrolase fold enzymes from plants, we are able to engineer proteins with broader chemoselectivity, altered enantioselectivity, and increased stability. A number of ancestral α/β-hydrolases fold proteins were reconstructed in one set of experiments. These were more likely than related modern proteins to have relaxed chemoselectivities and, in one case, was more useful for synthesizing medicinally important molecules. Relative to modern enzymes, ancestral enzymes near functional branch points could catalyze more esterase and hydroxynitrile lyase reactions, as well as a number of other types of reactions: decarboxylation, Michael addition, γ-lactam hydrolysis, and 1,5-diketone hydrolysis. This finding helps to demonstrate the important role that enzyme promiscuity plays in the evolution of new enzymes. Additional experiments and structural analysis on one of these reconstructed ancestral enzymes, the early hydroxynitrile lyase HNL1 found that it is both more thermostable and more promiscuous than its modern relatives, HbHNL and MeHNL. X-ray crystallographic studies revealed, counterintuitively, that larger amino acids in the active site of the ancestor actually increased the size of the substrate binding pocket relative to modern relatives. To take advantage of the promiscuity observed in HNL1, it was used in the asymmetric synthesis of a precursor for the important pharmaceutical propranolol. Another set of experiments altered enantioselectivity by making phylogenetically informed mutations. The active sites from two related hydroxynitrile lyases, HbHNL and AtHNL, were modified to resemble their last common ancestor. This resulted in altered enantioselectivity, and in the case of AtHNL, reversed enantioselectivity. Surprisingly modeling suggested that some of these mutants use a previously undescribed mechanism. This may have been the extinct ancestral mechanism that served as an evolutionary stepping stone that allowed descendant lineages to diverge to either the S-HNL mechanism used by HbHNL, or the R-HNL mechanism used by AtHNL. A final set of experiments used a variety of methods to identify stabilizing mutations in another plant α/β-hydrolase, SABP2. All of the methods were able to identify stabilizing mutations. The most stabilizing mutations were identified by methods that used no structural information. Random mutagenesis identified highly stabilizing mutations, but required screening thousands of mutants. The most efficient approaches were found to be those that used sequence information from either one stable homolog, or the consensus of many homologs, to identify potential stabilizing mutations. Residues that evolution has conserved are often important for stabilizing a protein. We created a software application, Consensus Finder, to automate the process of identifying stabilizing mutations by consensus

Test, Reliability and Functional Safety Trends for Automotive System-on-Chip

This paper encompasses three contributions by industry professionals and university researchers. The contributions describe different trends in automotive products, including both manufacturing test and run-time reliability strategies. The subjects considered in this session deal with critical factors, from optimizing the final test before shipment to market to in-field reliability during operative life

Tutorials at PPSN 2016

PPSN 2016 hosts a total number of 16 tutorials covering a broad range of current research in evolutionary computation. The tutorials range from introductory to advanced and specialized but can all be attended without prior requirements. All PPSN attendees are cordially invited to take this opportunity to learn about ongoing research activities in our field

Learning a formula of interpretability to learn interpretable formulas

Many risk-sensitive applications require Machine Learning (ML) models to be interpretable. Attempts to obtain interpretable models typically rely on tuning, by trial-and-error, hyper-parameters of model complexity that are only loosely related to interpretability. We show that it is instead possible to take a meta-learning approach: an ML model of non-trivial Proxies of Human Interpretability (PHIs) can be learned from human feedback, then this model can be incorporated within an ML training process to directly optimize for interpretability. We show this for evolutionary symbolic regression. We first design and distribute a survey finalized at finding a link between features of mathematical formulas and two established PHIs, simulatability and decomposability. Next, we use the resulting dataset to learn an ML model of interpretability. Lastly, we query this model to estimate the interpretability of evolving solutions within bi-objective genetic programming. We perform experiments on five synthetic and eight real-world symbolic regression problems, comparing to the traditional use of solution size minimization. The results show that the use of our model leads to formulas that are, for a same level of accuracy-interpretability trade-off, either significantly more or equally accurate. Moreover, the formulas are also arguably more interpretable. Given the very positive results, we believe that our approach represents an important stepping stone for the design of next-generation interpretable (evolutionary) ML algorithms