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Conventional genetic programming (GP) can guarantee only that synthesized programs pass tests given by the provided input-output examples. The alternative to such a test-based approach is synthesizing programs by formal specification, typically realized with exact, nonheuristic algorithms. In this article, we build on our earlier study on Counterexample-Based Genetic Programming (CDGP), an evolutionary heuristic that synthesizes programs from formal specifications. The candidate programs in CDGP undergo formal verification with a Satisfiability Modulo Theory (SMT) solver, which results in counterexamples that are subsequently turned into tests and used to calculate fitness. The original CDGP is extended here with a fitness threshold parameter that decides which programs should be verified, a more rigorous mechanism for turning counterexamples into tests, and other conceptual and technical improvements. We apply it to 24 benchmarks representing two domains: the linear integer arithmetic (LIA) and the string manipulation (SLIA) problems, showing that CDGP can reliably synthesize provably correct programs in both domains. We also confront it with two state-of-the art exact program synthesis methods and demonstrate that CDGP effectively trades longer synthesis time for smaller program size.

Throughout centuries philosophers have attempted to understand the disparity between the conscious experience and the material world -i.e., the problem of consciousness and the apparent mind-body dualism. Achievements in the fields of biology, neurology, and information science in the last century granted us more insight into processes that govern our minds. While there are still many mysteries to be solved when it comes to fully understanding the inner workings of our brains, new discoveries suggest stepping away from the metaphysical philosophy of mind, and closer to the computational viewpoint. In light of the advent of strong artificial intelligence and the development of increasingly complex artificial life models and simulations, we need a well-defined, formal theory of consciousness. In order to facilitate this, in this work we introduce mappism. Mappism is a framework in which alternative views on consciousness can be formally expressed in a uniform way, thus allowing one to analyze and compare existing theories, and enforcing the use of the language of mathematics, i.e, explicit functions and variables. Using this framework, we describe classical and artificial life approaches to consciousness.

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International audienceIn this paper we study differences between contiguous and non-contiguous parallel task schedules. Parallel tasks can be executed on more than one processor simultaneously. In the contiguous schedules, indices of the processors assigned to a task must be a sequence of consecutive numbers.In the non-contiguous schedules, processor indices may be arbitrary. Optimum non-preemptive schedules are considered.Given a parallel task instance, the optimum contiguous and non-contiguous schedules can be of different lengths.We analyze minimal instances where such a difference may arise, provide bounds on the difference of the two schedules lengths, and prove that deciding whether the difference in schedule length exists is NP-complete