user2713: Every chess engine I've ever heard of (including all I found listed on Wikipedia) uses brute-force search with an evaluation function (minmax algorithm) to decide on its move.
This is not how most humans approach the game, employing general pattern recognition instead, so in principle, it would be possible for computers to do the same.
Is there any chess engine that does not rely on the brute-force approach to find its moves?

user8309: Claude Shannon proposed two types of algorithms for creating chess engines.  A "type A" engine examines all possible moves to some finite depth, minimaxes the tree, and then plays the move with the highest evaluation from the minimaxed tree (a.k.a. brute force).  Type B engines limit their search to only a subset of possible moves based on some criteria.  I believed he favoured Type B as more promising.
The engines that were created in the 1970's (eg. Hitech, Kaissa) tended to be pure brute force with no pruning or just alpha-beta, but people soon saw the value of pruning the tree of moves and lines that were unlikely to prove strong.  Almost all recent engines prune the tree of lines that are clearly weaker (alpha-beta), and most engines use various types of forward pruning as well (futility, late move reduction, null move, razoring).  In that sense, there aren't many engines that use pure brute force anymore.
In the 1970's, Botvinnik was working on an engine called Pioneer conceived around the notion of attack paths which would have been evaluation guided.  It never reached the point where it could play a full game of chess.
In the 1990's, Chris Wittington spoke out in favour of using incorporating more chess knowledge, and created a program called Chess System Tal which was fairly strong for its time.
Kasparov, Anand and Tord Romstad have all noted that Hiarcs seems to have a more detailed evaluation than many of the top engines whose strength comes from a fast search.

user10810: Its sort of debatable if you can call a heuristic based search and evaluate approach as brute-force. Most of top-tier chess engines today follow a rules-based approach to evaluate a position and a rules based search function to prune moves.
This is actually not guaranteed to pick the "global optimal" move, however these moves are good enough for purpose. In this sense most chess engines are using an approximation on the global optimum and actually getting by.
To date, we haven't many chess engines succeed at the top level using a different approach, at least not on cheap hardware.

user825: I'd like add details to @Ian_Bush's answer on Giraffe.
In @Ian_Bush's answer, it's noted that Giraffe doesn't use brute-force computation. This is not right, because Giraffe is still an alpha-beta (nega-max) engine. The only difference to a standard engine is that the evaluation function is tuned automatically by deep-learning. Therefore, the engine learns how to play by itself.
Traditionally, engine programmer self-tunes parameters in an engine. I've done a lot myself. For example, how much weight should you give to a bishop and a knight? 3.0? 3.1? 3.2? It's hard to tell.
Giraffe approaches the problem in a much smarter way. It starts off with some initial values. The engine uses the gradient ascent algorithm to tune those values. We don't have to explicitly code how much weight a queen should be in the code. This is what we mean "learning". It doesn't mean that the engine can play chess without searching.
EDIT: Giraffe models the tree nodes as probability that they fall into the principal variatation. Check the paper for details. I personally don't believe this approach, and the paper shows little evidence how useful it would be.

user7726: You might take a look at Giraffe which was recently in the news:
https://thestack.com/iot/2015/09/14/neural-network-chess-computer-abandons-brute-force-for-selective-human-approach/
The hype is that in 3 days it taught itself the game and reached IM level. On the other hand the research is at
http://arxiv.org/abs/1509.01549

user2359: There were attempts back in the 1980s to write chess engines with knowledge bases that would pick candidate moves like humans, but they were unsuccessful. The problem is that human pattern matching is difficult to put into words, so creating the rules for the knowledge base was extremely difficult.
Training a neural network to pick candidate moves seems like a promising line of research. Here and here might be two pertinent papers. (FWIW, It is not my field of Comp Sci)

2713: <p>Every chess engine I've ever heard of (including all I found listed on Wikipedia) uses brute-force search with an evaluation function (minmax algorithm) to decide on its move.</p>

<p>This is not how most humans approach the game, employing general pattern recognition instead, so in principle, it would be possible for computers to do the same.</p>

<p>Is there any chess engine that does not rely on the brute-force approach to find its moves?</p>
 8309: <p>Claude Shannon proposed two types of algorithms for creating chess engines.  A "type A" engine examines all possible moves to some finite depth, minimaxes the tree, and then plays the move with the highest evaluation from the minimaxed tree (a.k.a. brute force).  Type B engines limit their search to only a subset of possible moves based on some criteria.  I believed he favoured Type B as more promising.</p>

<p>The engines that were created in the 1970's (eg. Hitech, Kaissa) tended to be pure brute force with no pruning or just alpha-beta, but people soon saw the value of pruning the tree of moves and lines that were unlikely to prove strong.  Almost all recent engines prune the tree of lines that are clearly weaker (alpha-beta), and most engines use various types of forward pruning as well (futility, late move reduction, null move, razoring).  In that sense, there aren't many engines that use pure brute force anymore.</p>

<p>In the 1970's, Botvinnik was working on an engine called Pioneer conceived around the notion of attack paths which would have been evaluation guided.  It never reached the point where it could play a full game of chess.</p>

<p>In the 1990's, Chris Wittington spoke out in favour of using incorporating more chess knowledge, and created a program called Chess System Tal which was fairly strong for its time.</p>

<p>Kasparov, Anand and Tord Romstad have all noted that Hiarcs seems to have a more detailed evaluation than many of the top engines whose strength comes from a fast search.</p>
 10810: <p>Its sort of debatable if you can call a heuristic based search and evaluate approach as brute-force. Most of top-tier chess engines today follow a rules-based approach to evaluate a position and a rules based search function to prune moves.</p>

<p>This is actually not guaranteed to pick the "global optimal" move, however these moves are good enough for purpose. In this sense most chess engines are using an approximation on the global optimum and actually getting by.</p>

<p>To date, we haven't many chess engines succeed at the top level using a different approach, at least not on cheap hardware.</p>
 825: <p>I'd like add details to @Ian_Bush's answer on Giraffe.</p>

<p>In @Ian_Bush's answer, it's noted that Giraffe doesn't use brute-force computation. This is <b>not right</b>, because Giraffe is still an alpha-beta (nega-max) engine. The <b>only</b> difference to a standard engine is that the evaluation function is tuned automatically by deep-learning. Therefore, the engine learns how to play by itself.</p>

<p>Traditionally, engine programmer self-tunes parameters in an engine. I've done a lot myself. For example, how much weight should you give to a bishop and a knight? 3.0? 3.1? 3.2? It's hard to tell.</p>

<p>Giraffe approaches the problem in a much smarter way. It starts off with some initial values. The engine uses the gradient ascent algorithm to tune those values. We don't have to explicitly code how much weight a queen should be in the code. This is what we mean "learning". It doesn't mean that the engine can play chess <i>without</i> searching.</p>

<p><strong>EDIT</strong>: Giraffe models the tree nodes as probability that they fall into the principal variatation. Check the paper for details. I personally don't believe this approach, and the paper shows little evidence how useful it would be.</p>
 7726: <p>You might take a look at Giraffe which was recently in the news:</p>

<p><a href="https://thestack.com/iot/2015/09/14/neural-network-chess-computer-abandons-brute-force-for-selective-human-approach/" rel="nofollow noreferrer">https://thestack.com/iot/2015/09/14/neural-network-chess-computer-abandons-brute-force-for-selective-human-approach/</a></p>

<p>The hype is that in 3 days it taught itself the game and reached IM level. On the other hand the research is at</p>

<p><a href="http://arxiv.org/abs/1509.01549" rel="nofollow noreferrer">http://arxiv.org/abs/1509.01549</a></p>
 2359: <p>There were attempts back in the 1980s to write chess engines with knowledge bases that would pick candidate moves like humans, but they were unsuccessful. The problem is that human pattern matching is difficult to put into words, so creating the rules for the knowledge base was extremely difficult.</p>

<p>Training a neural network to pick candidate moves seems like a promising line of research. <a href="http://www.cs.utoronto.ca/~ilya/pubs/2008/go_paper.pdf">Here</a> and <a href="http://www.pradu.us/old/Nov27_2008/Buzz/research/parallel/movemap_heuristic.pdf">here</a> might be two pertinent papers. (FWIW, It is not my field of Comp Sci)</p>
