It is approximately 50 years since the first computational experiments were conducted in what has become known today as the field of Genetic Programming (GP), twenty years since John Koza named and popularised the method, and ten years since the first issue appeared of the Genetic Programming & Evolvable Machines journal. In particular, during the past two decades there has been a significant range and volume of development in the theory and application of GP, and in recent years the field has become increasingly applied. There remain a number of significant open issues despite the successful application of GP to a number of challenging real-world problem domains and progress in the development of a theory explaining the behavior and dynamics of GP. These issues must be addressed for GP to realise its full potential and to become a trusted mainstream member of the computational problem solving toolkit. In this paper we outline some of the challenges and open issues that face researchers and practitioners of GP.

LONDON – Scientists are getting closer to building life from scratch and technology pioneers are taking notice, with record sums moving into a field that could deliver novel drugs, materials, chemicals and even perfumes. Despite ethical and safety concerns, investors are attracted by synthetic biology's wide market potential and the plummeting cost of DNA synthesis, which is industrializing the writing of the genetic code that determines how organisms function. While existing biotechnology is already used to make medicines like insulin and genetically modified crops, synthesizing whole genes or genomes gives an opportunity for far more extensive changes. Matt Ocko, a Silicon Valley venture capitalist whose past investments include Facebook, Uber and Zynga, believes the emerging industry has passed the "epiphany" moment needed to prove it can deliver economic value. "Synthetic biology companies are now becoming more like the disruptive, industrial-scale value propositions that define any technology business," he said.

We present an algorithm that can anticipate tax evasion by modeling the co-evolution of tax schemes with auditing policies. Malicious tax non-compliance, or evasion, accounts for billions of lost revenue each year. Unfortunately when tax administrators change the tax laws or auditing procedures to eliminate known fraudulent schemes another potentially more profitable scheme takes it place. Modeling both the tax schemes and auditing policies within a single framework can therefore provide major advantages. In particular we can explore the likely forms of tax schemes in response to changes in audit policies.

Scientists are getting closer to building life from scratch and technology pioneers are taking notice, with record sums moving into a field that could deliver novel drugs, materials, chemicals and even perfumes. Despite ethical and safety concerns, investors are attracted by synthetic biology's wide market potential and the plummeting cost of DNA synthesis, which is industrialising the writing of the genetic code that determines how organisms function. While existing biotechnology is already used to make medicines like insulin and genetically modified crops, synthesising whole genes or genomes gives an opportunity for far more extensive changes. Alexander the friendly robot visits the Indoor Park in London (file pic). Technology pioneers are investing huge sums of money in all manner of innovative ideas.

"The intersection of biology and technology is a difficult place to be because of different cultures and languages, but I think we are breaking through some of those barriers," said Thomas Bostick, former head of the U.S. Army Corps of Engineers who now leads biotech firm Intrexon's environment unit.

Of the many great things promised by Crispr gene editing technology, the ability to eliminate disease by modifying organisms might just top the list. But doing that requires perfecting something called a gene drive. Think of gene drives as a means of supercharging evolution to, say, give an entire population of mosquitoes a gene that kills the Zika virus. The trouble is, organisms develop resistance to gene drives, much like they eventually outwit pesticides and antibiotics. Researchers dedicate no small amount of time and thought to creating gene drives that can outsmart evolution because the potential payoffs are so great.

Traditional Linear Genetic Programming (LGP) algorithms are based only on the selection mechanism to guide the search. Genetic operators combine or mutate random portions of the individuals, without knowing if the result will lead to a fitter individual. Probabilistic Model Building Genetic Programming (PMB-GP) methods were proposed to overcome this issue through a probability model that captures the structure of the fit individuals and use it to sample new individuals. This work proposes the use of LGP with a Stochastic Context-Free Grammar (SCFG), that has a probability distribution that is updated according to selected individuals. We proposed a method for adapting the grammar into the linear representation of LGP. Tests performed with the proposed probabilistic method, and with two hybrid approaches, on several symbolic regression benchmark problems show that the results are statistically better than the obtained by the traditional LGP.

I watched Ilya Sutskever's talk on their new evolutionary strategies paper. The reason this paper is fascinating is that they use a relatively dumb, simple stochastic method of optimisation that shouldn't really work well in practice, and show that it is actually competitive with SGD/back-propagation-based methods in RL. This is mainly due to the fact that it parallelizes so naturally. Evolution strategies (ES) is can be best described as a gradient descent method which uses gradients estimated from stochastic perturbations around the current parameter value. While the authors did comparisons in the context of RL, and there are many RL-specific advantages, here I'm focussing on ES as a general black-box optimisation method.

It's not easy to train a neural network. Even if they're not difficult to implement, it can take hours to get them ready no matter how much computing power you can use. OpenAI researchers may have a better solution: forgetting many of the usual rules. They've developed an evolution strategy (no, it doesn't relate much to biological evolution) that promises more powerful AI systems. Rather than use standard reinforcement training, they create a "black box" where they forget that the environment and neural networks are even involved.

We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black-box optimization by approximating the given objective function through a surrogate regression model. It is designed for both single- and multi-objective optimization with mixed continuous, categorical and conditional parameters. Additional features include multi-point batch proposal, parallelization, visualization, logging and error-handling. mlrMBO is implemented in a modular fashion, such that single components can be easily replaced or adapted by the user for specific use cases, e.g., any regression learner from the mlr toolbox for machine learning can be used, and infill criteria and infill optimizers are easily exchangeable. We empirically demonstrate that mlrMBO provides state-of-the-art performance by comparing it on different benchmark scenarios against a wide range of other optimizers, including DiceOptim, rBayesianOptimization, SPOT, SMAC, Spearmint, and Hyperopt.