28,337 research outputs found

    A simple two-module problem to exemplify building-block assembly under crossover

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    Theoretically and empirically it is clear that a genetic algorithm with crossover will outperform a genetic algorithm without crossover in some fitness landscapes, and vice versa in other landscapes. Despite an extensive literature on the subject, and recent proofs of a principled distinction in the abilities of crossover and non-crossover algorithms for a particular theoretical landscape, building general intuitions about when and why crossover performs well when it does is a different matter. In particular, the proposal that crossover might enable the assembly of good building-blocks has been difficult to verify despite many attempts at idealized building-block landscapes. Here we show the first example of a two-module problem that shows a principled advantage for cross-over. This allows us to understand building-block assembly under crossover quite straightforwardly and build intuition about more general landscape classes favoring crossover or disfavoring it

    Crossover Behaviour of 3-Species Systems with Mutations or Migrations

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    We study the ABC model in the cyclic competition and neutral drift versions, with mutations and migrations introduced into the model. When stochastic phenomena are taken into account, there are three distinct regimes in the model. (i) In the "fixation" regime, the first extinction time scales with the system size N and has an exponential distribution, with an exponent that depends on the mutation/migration probability per particle. (ii) In the "diversity" regime, the order parameter remains nonzero for very long times, and becomes zero only rarely, almost never for large system sizes. (iii) In the critical regime, the first extinction time has a power-law distribution with exponent -1. The transition corresponds to a crossover from diffusive behaviour to Gaussian fluctuations about a stable solution. The analytical results are checked against computer simulations of the model.Comment: 2nd version revised and refereed 14 pages, 5 figure

    Phase Transitions and Symmetry Breaking in Genetic Algorithms with Crossover

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    In this paper, we consider the role of the crossover operator in genetic algorithms. Specifically, we study optimisation problems that exhibit many local optima and consider how crossover affects the rate at which the population breaks the symmetry of the problem. As an example of such a problem, we consider the subset sum problem. In so doing, we demonstrate a previously unobserved phenomenon, whereby the genetic algorithm with crossover exhibits a critical mutation rate, at which its performance sharply diverges from that of the genetic algorithm without crossover. At this critical mutation rate, the genetic algorithm with crossover exhibits a rapid increase in population diversity. We calculate the details of this phenomenon on a simple instance of the subset sum problem and show that it is a classic phase transition between ordered and disordered populations. Finally, we show that this critical mutation rate corresponds to the transition between the genetic algorithm accelerating or preventing symmetry breaking and that the critical mutation rate represents an optimum in terms of the balance of exploration and exploitation within the algorithm

    Repeated sequences in linear genetic programming genomes

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    Biological chromosomes are replete with repetitive sequences, micro satellites, SSR tracts, ALU, etc. in their DNA base sequences. We started looking for similar phenomena in evolutionary computation. First studies find copious repeated sequences, which can be hierarchically decomposed into shorter sequences, in programs evolved using both homologous and two point crossover but not with headless chicken crossover or other mutations. In bloated programs the small number of effective or expressed instructions appear in both repeated and nonrepeated code. Hinting that building-blocks or code reuse may evolve in unplanned ways. Mackey-Glass chaotic time series prediction and eukaryotic protein localisation (both previously used as artificial intelligence machine learning benchmarks) demonstrate evolution of Shannon information (entropy) and lead to models capable of lossy Kolmogorov compression. Our findings with diverse benchmarks and GP systems suggest this emergent phenomenon may be widespread in genetic systems

    Crystal Structure of an Anti-Ang2 CrossFab Demonstrates Complete Structural and Functional Integrity of the Variable Domain.

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    Bispecific antibodies are considered as a promising class of future biotherapeutic molecules. They comprise binding specificities for two different antigens, which may provide additive or synergistic modes of action. There is a wide variety of design alternatives for such bispecific antibodies, including the "CrossMab" format. CrossMabs contain a domain crossover in one of the antigen-binding (Fab) parts, together with the "knobs-and-holes" approach, to enforce the correct assembly of four different polypeptide chains into an IgG-like bispecific antibody. We determined the crystal structure of a hAng-2-binding Fab in its crossed and uncrossed form and show that CH1-CL-domain crossover does not induce significant perturbations of the structure and has no detectable influence on target binding

    A randomized, phase II study of afatinib versus cetuximab in metastatic or recurrent squamous cell carcinoma of the head and neck.

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    BackgroundAfatinib is an oral, irreversible ErbB family blocker that has shown activity in epidermal growth factor receptor (EGFR)-mutated lung cancer. We hypothesized that the agent would have greater antitumor activity compared with cetuximab in recurrent or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) patients, whose disease has progressed after platinum-containing therapy.Patients and methodsAn open-label, randomized, phase II trial was conducted in 43 centers; 124 patients were randomized (1 : 1) to either afatinib (50 mg/day) or cetuximab (250 mg/m(2)/week) until disease progression or intolerable adverse events (AEs) (stage I), with optional crossover (stage II). The primary end point was tumor shrinkage before crossover assessed by investigator (IR) and independent central review (ICR).ResultsA total of 121 patients were treated (61 afatinib, 60 cetuximab) and 68 crossed over to stage II (32 and 36 respectively). In stage I, mean tumor shrinkage by IR/ICR was 10.4%/16.6% with afatinib and 5.4%/10.1% with cetuximab (P = 0.46/0.30). Objective response rate was 16.1%/8.1% with afatinib and 6.5%/9.7% with cetuximab (IR/ICR). Comparable disease control rates were observed with afatinib (50%) and cetuximab (56.5%) by IR; similar results were seen by ICR. Most common grade ≥3 drug-related AEs (DRAEs) were rash/acne (18% versus 8.3%), diarrhea (14.8% versus 0%), and stomatitis/mucositis (11.5% versus 0%) with afatinib and cetuximab, respectively. Patients with DRAEs leading to treatment discontinuation were 23% with afatinib and 5% with cetuximab. In stage II, disease control rate (IR/ICR) was 38.9%/33.3% with afatinib and 18.8%/18.8% with cetuximab.ConclusionAfatinib showed antitumor activity comparable to cetuximab in R/M HNSCC in this exploratory phase II trial, although more patients on afatinib discontinued treatment due to AEs. Sequential EGFR/ErbB treatment with afatinib and cetuximab provided sustained clinical benefit in patients after crossover, suggesting a lack of cross-resistance

    Establishing parameters for problem difficulty in permutation-based genetic algorithms

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    This thesis examines the performance of genetic algorithm (GA) crossover techniques within two problems: n-queens with poison (NQWP) and processor scheduling (PS). Each problem was analyzed at sizes of 32, 64, and 128, referring to number of queens to be placed and number of single-time-unit processes to be scheduled, respectively. The specific crossover techniques studied were cycle crossover, order crossover, partially mapped crossover, merging crossover, and one-point, two-point, and uniform signature representation crossover, in addition to various greedy approaches. In conjunction with tests that vary crossover techniques, experimentation was performed to determine what percentage of problem constraints (poisoned squares for NQWP or precedence relationships between tasks for PS) makes the problems most difficult to solve, that is, the constraint densities at which optimal solutions require the highest number of GA fitness evaluations. While minor fluctuations in difficulty occur upon variations in fitness function and problem size, the NQWP problem is most difficult around a constraint density of 0.8 and the PS problem is most difficult around constraint densities of 0.2 to 0.3. Even within an individual problem, one crossover technique does not irreproachably outperform others. However, cycle crossover stands out in its performance in the PS problem while merging crossover and uniform signature crossovers most often perform well for NQWP
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