Program synthesis is the process of generating a computer program following a
set of specifications, which can be a high-level description of the problem
and/or a set of input-output examples. The synthesis can be modeled as a search
problem in which the search space is the set of all the programs valid under a
grammar. As the search space is vast, brute force is usually not viable and
search heuristics, such as genetic programming, also have difficulty navigating
it without any guidance. In this paper we present HOTGP, a new genetic
programming algorithm that synthesizes pure, typed, and functional programs.
HOTGP leverages the knowledge provided by the rich data-types associated with
the specification and the built-in grammar to constrain the search space and
improve the performance of the synthesis. The grammar is based on Haskell's
standard base library (the synthesized code can be directly compiled using any
standard Haskell compiler) and includes support for higher-order functions,
$\lambda$-functions, and parametric polymorphism. Experimental results show
that, when compared to $6$ state-of-the-art algorithms using a standard set of
benchmarks, HOTGP is competitive and capable of synthesizing the correct
programs more frequently than any other of the evaluated algorithms.

本文介绍了一种新的基于 Haskell 的基因编程算法 HOTGP，它利用规约的数据类型来约束搜索空间，更好地合成类型良好的纯函数编程。经验结果表明，与其他六种算法相比，HOTGP 在综合正确程序的能力上更为优异。

HOTGP -- 高阶类型遗传规划

HOTGP -- Higher-Order Typed Genetic Programming

Choosing a suitable algorithm from the myriads of different search heuristics
is difficult when faced with a novel optimization problem. In this work, we
argue that the purely academic question of what could be the best possible
algorithm in a certain broad class of black-box optimizers can give fruitful
indications in which direction to search for good established optimization
heuristics. We demonstrate this approach on the recently proposed DLB
benchmark, for which the only known results are $O(n^3)$ runtimes for several
classic evolutionary algorithms and an $O(n^2 \log n)$ runtime for an
estimation-of-distribution algorithm. Our finding that the unary unbiased
black-box complexity is only $O(n^2)$ suggests the Metropolis algorithm as an
interesting candidate and we prove that it solves the DLB problem in quadratic
time. Since we also prove that better runtimes cannot be obtained in the class
of unary unbiased algorithms, we shift our attention to algorithms that use the
information of more parents to generate new solutions. An artificial algorithm
of this type having an $O(n \log n)$ runtime leads to the result that the
significance-based compact genetic algorithm (sig-cGA) can solve the DLB
problem also in time $O(n \log n)$. Our experiments show a remarkably good
performance of the Metropolis algorithm, clearly the best of all algorithms
regarded for reasonable problem sizes.

本论文旨在寻找用于黑盒优化的最佳算法，研究表明 Metropolis 算法在求解 DLB 问题时表现最佳，并且比所有其他算法都要快。

基于复杂理论的正确算法选择

Choosing the Right Algorithm With Hints From Complexity Theory

Effective solving of constraint problems often requires choosing good or
specific search heuristics. However, choosing or designing a good search
heuristic is non-trivial and is often a manual process. In this paper, rather
than manually choosing/designing search heuristics, we propose the use of
bandit-based learning techniques to automatically select search heuristics. Our
approach is online where the solver learns and selects from a set of heuristics
during search. The goal is to obtain automatic search heuristics which give
robust performance. Preliminary experiments show that our adaptive technique is
more robust than the original search heuristics. It can also outperform the
original heuristics.

通过采用基于赌博机的学习技术来自动选择搜索启发式方法，我们的方法在线学习和选择一组搜索启发式方法，旨在获得具有健壮性和比原先性能更好的自适应搜索启发式方法。初步实验表明，自适应技术比原始搜索启发式方法更为健壮且性能更好。